diff --git a/Quantumcoders/README.md b/Quantumcoders/README.md new file mode 100644 index 00000000..ca7803a6 --- /dev/null +++ b/Quantumcoders/README.md @@ -0,0 +1,45 @@ +# CASAhackathon +**Team Name** - Quantum coders + +**Problem Statement** - AI enhanced retail shopping + +**Team Leader Email** - ravishankarvaradharajan@gmail.com + +**A Brief of the Prototype:** + +Chief AI Shopping Assistant (CASA) is a proactive shopping companion featuring learning adaptation, tailoring each user's experience by continuously adapting to evolving preferences. It also provides Instore analysis and helps with visual merchandising. +It will help us track deadzones in the store. The analysis will be displayed on a dashboard which contain Customer gaze analysis, Foot traffic, User Interaction with products. It will also helps in predicting the sales and keep up with the trends. +Our online assistant powered by LLM will provide suggestions based on items in the cart, user preferences and user profile. It also provides a chat interface to have a back and forth conversation to help the user figure out their fashion needs. + +**Instore flow:** + +![image](https://github.com/no-one-really/CASAhackathon/assets/58998511/5494b8b6-9bb9-42a1-834f-4e100f03e433) + +**Online recommendation flow:** + +![image](https://github.com/no-one-really/CASAhackathon/assets/58998511/397447e0-285b-45a1-9fa5-a7e5f60f3c9e) + + +**Tech Stack:** + +![image](https://github.com/no-one-really/CASAhackathon/assets/58998511/1b3c9f27-1c66-4756-85f7-afc8548398b1) + + +**Step-by-Step Code Execution Instructions:** + +Step 1: Install requirements +```pip install - requirements.txt``` + +Step2: +Host the Django server by runnning python manage.py runserver + +Step3: +setup ngrok https://ngrok.com/docs/getting-started/ + +Step4: +delpoy the model by running start.js, serv.py and recieve.py + +**Future Scope:** + + To integrate Large Language-and-Vision Assistants to provide better shopping assistance and gain better insights on the customer needs + diff --git a/Quantumcoders/__init__.py b/Quantumcoders/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/Quantumcoders/admin.py b/Quantumcoders/admin.py new file mode 100644 index 00000000..ea5d68b7 --- /dev/null +++ b/Quantumcoders/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Quantumcoders/apps.py b/Quantumcoders/apps.py new file mode 100644 index 00000000..446b8169 --- /dev/null +++ b/Quantumcoders/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ShopConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "shop" diff --git a/Quantumcoders/asgi.py b/Quantumcoders/asgi.py new file mode 100644 index 00000000..d0a93e3b --- /dev/null +++ b/Quantumcoders/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for mysite project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mysite.settings") + +application = get_asgi_application() diff --git a/Quantumcoders/db.sqlite3 b/Quantumcoders/db.sqlite3 new file mode 100644 index 00000000..e69de29b diff --git a/Quantumcoders/manage.py b/Quantumcoders/manage.py new file mode 100644 index 00000000..71efd103 --- /dev/null +++ b/Quantumcoders/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mysite.settings") + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == "__main__": + main() diff --git a/Quantumcoders/models.py b/Quantumcoders/models.py new file mode 100644 index 00000000..fd18c6ea --- /dev/null +++ b/Quantumcoders/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Quantumcoders/settings.py b/Quantumcoders/settings.py new file mode 100644 index 00000000..a4fab38d --- /dev/null +++ b/Quantumcoders/settings.py @@ -0,0 +1,126 @@ +""" +Django settings for mysite project. + +Generated by 'django-admin startproject' using Django 4.2.7. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.2/ref/settings/ +""" + +from pathlib import Path +import os + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.2/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = "django-insecure-4z=&32ebvzt6&%&%#fx_%g)n-@@qj)strt-m^!vnr39!bzv^yt" + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + "django.contrib.admin", + "django.contrib.auth", + "django.contrib.contenttypes", + "django.contrib.sessions", + "django.contrib.messages", + "django.contrib.staticfiles", +] + +MIDDLEWARE = [ + "django.middleware.security.SecurityMiddleware", + "django.contrib.sessions.middleware.SessionMiddleware", + "django.middleware.common.CommonMiddleware", + "django.middleware.csrf.CsrfViewMiddleware", + "django.contrib.auth.middleware.AuthenticationMiddleware", + "django.contrib.messages.middleware.MessageMiddleware", + "django.middleware.clickjacking.XFrameOptionsMiddleware", +] + +ROOT_URLCONF = "mysite.urls" + +TEMPLATES = [ + { + "BACKEND": "django.template.backends.django.DjangoTemplates", + "DIRS": [os.path.join(BASE_DIR,'templates')], + "APP_DIRS": True, + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.debug", + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "django.contrib.messages.context_processors.messages", + ], + }, + }, +] + +WSGI_APPLICATION = "mysite.wsgi.application" + + +# Database +# https://docs.djangoproject.com/en/4.2/ref/settings/#databases + +DATABASES = { + "default": { + "ENGINE": "django.db.backends.sqlite3", + "NAME": BASE_DIR / "db.sqlite3", + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.2/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator", + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.2/topics/i18n/ + +LANGUAGE_CODE = "en-us" + +TIME_ZONE = "UTC" + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.2/howto/static-files/ + +STATIC_URL = "static/" +STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')] +STATIC_ROOT = os.path.join(BASE_DIR,"assets") + +# Default primary key field type +# https://docs.djangoproject.com/en/4.2/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" diff --git a/Quantumcoders/tests.py b/Quantumcoders/tests.py new file mode 100644 index 00000000..de8bdc00 --- /dev/null +++ b/Quantumcoders/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Quantumcoders/urls.py b/Quantumcoders/urls.py new file mode 100644 index 00000000..93c072cc --- /dev/null +++ b/Quantumcoders/urls.py @@ -0,0 +1,10 @@ +from django.urls import path + +from . import views + +urlpatterns =[ +path("", views.home, name="home"), +path("casa", views.casa, name="casa"), +path("cart", views.cart, name="cart"), +path("test12", views.test12, name="test12"), +] diff --git a/Quantumcoders/views.py b/Quantumcoders/views.py new file mode 100644 index 00000000..539ffccf --- /dev/null +++ b/Quantumcoders/views.py @@ -0,0 +1,43 @@ +from django.shortcuts import render +from django.http import HttpResponse + +shplist=["T-shirt Contrast Pocket","Basic Flowing Scarf"] +rcmdlist=["black shoe","Basic Flowing Scarf"] + +itemDescription={"T-shirt Contrast Pocket":['$30','static/img/shopping-cart/cart-1.jpg'],"Basic Flowing Scarf":["$47.00","static/img/shopping-cart/cart-3.jpg"], +"black shoe":["$32.50","static/img/shopping-cart/cart-2.jpg"]} +fullDetail=[] +for it in shplist: + currlst=[] + currlst.append(it) + for jk in itemDescription[it]: + currlst.append(jk) + fullDetail.append(currlst) +rcdflist=[] +for it in rcmdlist: + currlst=[] + currlst.append(it) + for jk in itemDescription[it]: + currlst.append(jk) + rcdflist.append(currlst) + +# Create your views here. +def home(request): + return render(request,'index.html') + +def casa(request): + return render(request,'tgg.html') + +def cart(request): + return render(request,'shopping-cart.html',{"lst":fullDetail,"rlst":rcdflist}) + + + + + +# @register.filter +# def get_index(lst, i): +# return lst[i] + +def test12(request): + return render(request,"test12.html",{"lst":shplist,"rlst":rcdflist}) diff --git a/Quantumcoders/wsgi.py b/Quantumcoders/wsgi.py new file mode 100644 index 00000000..78134f21 --- /dev/null +++ b/Quantumcoders/wsgi.py @@ -0,0 +1,16 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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "lida 0.0.10 requires fastapi, which is not installed.\n", + "lida 0.0.10 requires kaleido, which is not installed.\n", + "lida 0.0.10 requires uvicorn, which is not installed.\n", + "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.1.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "# Need some pre-requisites\n", + "\n", + "!pip install langchain\n", + "!pip install huggingface_hub\n", + "!pip install sentence_transformers\n", + "!pip install unstructured[local-inference] -q\n", + "!pip install -q faiss-cpu" + ] + }, + { + "cell_type": "code", + "source": [ + "# FAISS needed to store the vector representations of PDF text data from UnstructuredPDFLoader\n", + "# QA will provide me with answer based data from PDF provided, to the prompt given\n", + "\n", + "from langchain.document_loaders import UnstructuredPDFLoader\n", + "from langchain.vectorstores import FAISS\n", + "from langchain.chains.question_answering import load_qa_chain\n" + ], + "metadata": { + "id": "c61wykhqRPUw" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# PLEASE PROVIDE HUGGINGFACE TOKEN, SO THAT WE ARE ABLE TO USE HUGGINGFACE MODELS\n", + "\n", + "import os\n", + "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"PUT_YOUR_HUGGING_FACE_TOKEN_HERE\"" + ], + "metadata": { + "id": "7whTyEnWRs6Q" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Load the document of where the data is.. In my case., Clothes Catalogue\n", + "\n", + "loader = UnstructuredPDFLoader(\"/content/doc2.pdf\")\n", + "document = loader.load()" + ], + "metadata": { + "id": "Wm1hwY_LRWng", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c79a78ad-134e-4e72-d1a7-912776bdbe59" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Take a document and split it into chunks of a specified size while considering overlap and using defined separators.\n", + "# Useful for processing large text documents in smaller, more manageable pieces.\n", + "\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap=0, separators=[\" \", \",\", \"\\n\"])\n", + "docs = text_splitter.split_documents(document)" + ], + "metadata": { + "id": "uXlo_SH7RWiH" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Numerical representations of words, sentences, or documents that capture semantic information are done with HuggingFaceEmbeddings\n", + "# Creating a FAISS index from the embedded documents, which allows for efficient similarity search based on the learned embeddings.\n", + "\n", + "from langchain.embeddings import HuggingFaceEmbeddings\n", + "\n", + "embedding = HuggingFaceEmbeddings()\n", + "db = FAISS.from_documents(docs, embedding)" + ], + "metadata": { + "id": "N7NpRjWbRWfl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 465, + "referenced_widgets": [ + "23607968bc4d49b786b77b3ea34b074e", + "d891ab2e06974741856591110be2c4c8", + "b0750223d8fc4a19b9cae4bea1999612", + "6510989ec2714da9a503b5bfe75bf71a", + "641deb3c3fca4a8e9473a130d28d74d1", + 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simplicity\n", + " face = faces[0]\n", + " \n", + " # Detect facial landmarks\n", + " landmarks = landmarks_predictor(gray, face)\n", + " \n", + " # Extract relevant landmarks for orientation estimation\n", + " left_eye = (landmarks.part(36).x, landmarks.part(36).y)\n", + " right_eye = (landmarks.part(45).x, landmarks.part(45).y)\n", + " nose_tip = (landmarks.part(30).x, landmarks.part(30).y)\n", + " \n", + " # Calculate the midpoint of the eyes\n", + " eyes_midpoint = (\n", + " (left_eye[0] + right_eye[0]) // 2, (left_eye[1] + right_eye[1]) // 2)\n", + " \n", + " # Calculate the horizontal distance between the midpoint of the eyes and the nose tip\n", + " horizontal_distance = abs(nose_tip[0] - eyes_midpoint[0])\n", + " \n", + " # Set a threshold for the horizontal distance to classify the orientation\n", + " if horizontal_distance < 10: # Adjust the threshold as needed\n", + " orientation = \"Keyboard\"\n", + " elif nose_tip[0] > eyes_midpoint[0]:\n", + " orientation = \"phone\"\n", + " else:\n", + " orientation = \"laptop\"\n", + " \n", + " # Display the result on the frame\n", + " cv2.putText(frame, f\"Customer 102 is looking at {orientation}\", (\n", + " 10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n", + " \n", + " # Display the frame\n", + " cv2.imshow(\"Face Orientation Analysis\", frame)\n", + " \n", + " # Break the loop if 'q' is pressed\n", + " if cv2.waitKey(1) & 0xFF == ord('q'):\n", + " break\n", + " \n", + " # Release the camera and close all OpenCV windows\n", + " cap.release()\n", + " cv2.destroyAllWindows()\n", + " \n", + " \n", + "if __name__ == \"__main__\":\n", + " detect_face_orientation()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6ac09db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gaze_estimation.py b/gaze_estimation.py new file mode 100644 index 00000000..732300e1 --- /dev/null +++ b/gaze_estimation.py @@ -0,0 +1,134 @@ +import cv2 +import dlib +import torch + +# Use Intel Optimized TensorFlow +import tensorflow as tf +print(tf.__version__) + +# Use Intel Optimized PyTorch +print(torch.__config__.show()) + + +def detect_face_orientation(video_source=0): + # Rest of your code remains unchanged... + # (Please note that you need to download the shape_predictor_68_face_landmarks.dat file) + + while True: + # Capture frame-by-frame + ret, frame = cap.read() + if not ret: + print("Error reading frame.") + break + + # Convert the frame to grayscale for face detection + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # Detect faces in the frame + faces = face_detector(gray) + + # Check if a face is detected + if len(faces) > 0: + # Assume only one face in the frame for simplicity + face = faces[0] + + # Detect facial landmarks + landmarks = landmarks_predictor(gray, face) + + # Extract relevant landmarks for orientation estimation + # Rest of your code remains unchanged... + + # Release the camera and close all OpenCV windows + cap.release() + cv2.destroyAllWindows() + + +if __name__ == "__main__": + # Initialize the face detector from dlib + face_detector = dlib.get_frontal_face_detector() + landmarks_predictor = dlib.shape_predictor( + "shape_predictor_68_face_landmarks.dat") # You need to download this file + + # Open a connection to the video source (0 for default camera) + cap = cv2.VideoCapture(0) + + detect_face_orientation() + + + + + + + +# import cv2 +# import dlib + + +# def detect_face_orientation(video_source=0): +# # Open a connection to the video source (0 for default camera) +# cap = cv2.VideoCapture(video_source) + +# # Initialize the face detector from dlib +# face_detector = dlib.get_frontal_face_detector() +# landmarks_predictor = dlib.shape_predictor( +# "shape_predictor_68_face_landmarks.dat") # You need to download this file + +# while True: +# # Capture frame-by-frame +# ret, frame = cap.read() +# if not ret: +# print("Error reading frame.") +# break + +# # Convert the frame to grayscale for face detection +# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + +# # Detect faces in the frame +# faces = face_detector(gray) + +# # Check if a face is detected +# if len(faces) > 0: +# # Assume only one face in the frame for simplicity +# face = faces[0] + +# # Detect facial landmarks +# landmarks = landmarks_predictor(gray, face) + +# # Extract relevant landmarks for orientation estimation +# left_eye = (landmarks.part(36).x, landmarks.part(36).y) +# right_eye = (landmarks.part(45).x, landmarks.part(45).y) +# nose_tip = (landmarks.part(30).x, landmarks.part(30).y) + +# # Calculate the midpoint of the eyes +# eyes_midpoint = ( +# (left_eye[0] + right_eye[0]) // 2, (left_eye[1] + right_eye[1]) // 2) + +# # Calculate the horizontal distance between the midpoint of the eyes and the nose tip +# horizontal_distance = abs(nose_tip[0] - eyes_midpoint[0]) + +# # Set a threshold for the horizontal distance to classify the orientation +# if horizontal_distance < 10: # Adjust the threshold as needed +# orientation = "Allen solly shirt" +# elif nose_tip[0] > eyes_midpoint[0]: +# orientation = "Bata shoe" +# else: +# orientation = "katespade" + +# # Display the result on the frame +# cv2.putText(frame, f"Customer 102 is looking at {orientation}", ( +# 10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) + +# # Display the frame +# cv2.imshow("Face Orientation Analysis", frame) + +# # Break the loop if 'q' is pressed +# if cv2.waitKey(1) & 0xFF == ord('q'): +# break + +# # Release the camera and close all OpenCV windows +# cap.release() +# cv2.destroyAllWindows() + + +# if __name__ == "__main__": +# detect_face_orientation() \ No newline at end of file diff --git a/home.html b/home.html new file mode 100644 index 00000000..3b2634d5 --- /dev/null +++ b/home.html @@ -0,0 +1 @@ +

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+ + + + + + + + + + + diff --git a/manage.py b/manage.py new file mode 100644 index 00000000..71efd103 --- /dev/null +++ b/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mysite.settings") + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == "__main__": + main() diff --git a/mistral.py b/mistral.py new file mode 100644 index 00000000..fbc89ce7 --- /dev/null +++ b/mistral.py @@ -0,0 +1,45 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer +import sys +statement = sys.argv[1] +prompt2 = sys.argv[2] +clr_st = statement.replace("#", " ") +clr_pmt = prompt2.replace("#", " ") +device = "cpu" + + +def find_nth_occurrence(text, target, n): + count = 0 + index = -1 + + while count < n: + index = text.find(target, index + 1) + + if index == -1: + break # Target not found + + count += 1 + + return index + + +target_string = "[/INST]" +nth_occurrence = 2 + +tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") +model = AutoModelForCausalLM.from_pretrained( + "mistralai/Mistral-7B-Instruct-v0.1") +messages = [ + {"role": "user", "content": "I need some assistance in IT"}, + {"role": "assistant", "content": clr_pmt}, + {"role": "user", "content": clr_st} + +] +encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") +# model.save_pretrained() +model_inputs = encodeds.to(device) +model.to(device) +generated_ids = model.generate( + model_inputs, max_new_tokens=1000, do_sample=True) +decoded = tokenizer.batch_decode(generated_ids) +result = find_nth_occurrence(decoded[0], target_string, nth_occurrence) +print(decoded[0][result+7:]) diff --git a/models.py b/models.py new file mode 100644 index 00000000..fd18c6ea --- /dev/null +++ b/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/mysite.zip b/mysite.zip new file mode 100644 index 00000000..e37f4fb1 Binary files /dev/null and b/mysite.zip differ diff --git a/recieve.py b/recieve.py new file mode 100644 index 00000000..790fe407 --- /dev/null +++ b/recieve.py @@ -0,0 +1,32 @@ +# Script B + +import socket +import sys +import json # Import the JSON module for serialization + +statement = sys.argv[1] +prompt2 = sys.argv[2] + + +def send_request(): + request = [statement, prompt2] + + client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + client.connect(('localhost', 8888)) + + # Serialize the list to a JSON string + request_json = json.dumps(request) + + # Send the request to the server + client.send(request_json.encode('utf-8')) + + # Receive the response from the server + response = client.recv(1024).decode('utf-8') + print(f"{response}") + + # Close the connection + client.close() + + +if __name__ == "__main__": + send_request() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..08468ed9 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,455 @@ +about-time==4.2.1 +absl-py==2.0.0 +accelerate==0.23.0 +aiobotocore @ file:///C:/b/abs_74o47svlua/croot/aiobotocore_1680004300264/work +aiofiles @ file:///C:/b/abs_9ex6mi6b56/croot/aiofiles_1683773603390/work +aiohttp @ file:///C:/ci_311/aiohttp_1676432932774/work +aioitertools @ file:///tmp/build/80754af9/aioitertools_1607109665762/work +aiosignal @ file:///tmp/build/80754af9/aiosignal_1637843061372/work +aiosqlite @ file:///C:/b/abs_9djc_0pyi3/croot/aiosqlite_1683773915844/work +alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work +alive-progress==3.1.4 +altair==5.1.2 +anaconda-catalogs @ file:///C:/b/abs_8btyy0o8s8/croot/anaconda-catalogs_1685727315626/work +anaconda-client==1.11.3 +anaconda-navigator==2.4.2 +anaconda-project @ file:///C:/ci_311/anaconda-project_1676458365912/work +annotated-types==0.6.0 +anyio==3.7.1 +appdirs==1.4.4 +argon2-cffi @ file:///opt/conda/conda-bld/argon2-cffi_1645000214183/work +argon2-cffi-bindings @ file:///C:/ci_311/argon2-cffi-bindings_1676424443321/work +arrow @ file:///C:/ci_311/arrow_1678249767083/work +asgiref==3.7.2 +astroid @ file:///C:/ci_311/astroid_1678740610167/work +astropy @ file:///C:/ci_311_rebuilds/astropy_1678996071858/work +asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work +astunparse==1.6.3 +async-timeout @ file:///C:/ci_311/async-timeout_1676431518331/work +atomicwrites==1.4.0 +attrs @ file:///C:/ci_311/attrs_1676422272484/work +autograd==1.6.2 +Automat @ file:///tmp/build/80754af9/automat_1600298431173/work +autopep8 @ file:///opt/conda/conda-bld/autopep8_1650463822033/work +Babel @ file:///C:/ci_311/babel_1676427169844/work +backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work +backports.functools-lru-cache @ file:///tmp/build/80754af9/backports.functools_lru_cache_1618170165463/work +backports.tempfile @ file:///home/linux1/recipes/ci/backports.tempfile_1610991236607/work +backports.weakref==1.0.post1 +bcrypt @ file:///C:/ci_311/bcrypt_1676435170049/work +beautifulsoup4 @ file:///C:/b/abs_0agyz1wsr4/croot/beautifulsoup4-split_1681493048687/work +binaryornot @ file:///tmp/build/80754af9/binaryornot_1617751525010/work +black @ file:///C:/b/abs_620t6ndje8/croot/black_1680737261963/work +bleach @ file:///opt/conda/conda-bld/bleach_1641577558959/work +bokeh @ file:///C:/b/abs_d01xodyb9e/croot/bokeh_1684534031183/work +boltons @ file:///C:/ci_311/boltons_1677729932371/work +boto3 @ file:///C:/ci_311_rebuilds/boto3_1678997157581/work +botocore @ file:///C:/ci_311/botocore_1676431559339/work +Bottleneck @ file:///C:/ci_311/bottleneck_1676500016583/work +brotlipy==0.7.0 +cachetools==5.3.2 +certifi @ file:///C:/b/abs_4a0polqwty/croot/certifi_1683875377622/work/certifi +cffi @ file:///C:/ci_311/cffi_1676423759166/work +chardet @ file:///C:/ci_311/chardet_1676436134885/work +charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work +click @ file:///C:/ci_311/click_1676433091657/work +cloudpickle @ file:///C:/b/abs_3796yxesic/croot/cloudpickle_1683040098851/work +clyent==1.2.2 +cma==3.2.2 +cmake==3.22.5 +colorama @ file:///C:/ci_311/colorama_1676422310965/work +colorcet @ file:///C:/ci_311/colorcet_1676440389947/work +coloredlogs==15.0.1 +comm @ file:///C:/ci_311/comm_1678376562840/work +conda @ file:///C:/b/abs_a1khk4epoo/croot/conda_1685025197412/work +conda-build @ file:///C:/b/abs_1f8uy1sady/croot/conda-build_1685026157582/work +conda-content-trust @ file:///C:/ci_311/conda-content-trust_1676467587370/work +conda-libmamba-solver @ file:///C:/b/abs_5a9xvt2a3k/croot/conda-libmamba-solver_1685032355900/work/src +conda-pack @ file:///tmp/build/80754af9/conda-pack_1611163042455/work +conda-package-handling @ file:///C:/b/abs_ce4_vcfd0y/croot/conda-package-handling_1685024800103/work +conda-repo-cli==1.0.41 +conda-token @ file:///Users/paulyim/miniconda3/envs/c3i/conda-bld/conda-token_1662660369760/work +conda-verify==3.4.2 +conda_index @ file:///C:/ci_311/conda-index_1678297406811/work +conda_package_streaming @ file:///C:/b/abs_88a7k_wmm1/croot/conda-package-streaming_1685019697115/work +config==0.5.1 +constantly==15.1.0 +contextlib2==21.6.0 +contourpy @ file:///C:/ci_311/contourpy_1676431756017/work +cookiecutter @ file:///opt/conda/conda-bld/cookiecutter_1649151442564/work +cryptography @ file:///C:/ci_311/cryptography_1679419210767/work +cssselect==1.1.0 +cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work +Cython==3.0.6 +cytoolz @ file:///C:/ci_311/cytoolz_1676436342770/work +daal4py==2023.1.1 +dask @ file:///C:/b/abs_23lvfodys3/croot/dask-core_1686782960052/work +datasets==2.14.5 +datashader @ file:///C:/b/abs_d7hw5cd3cy/croot/datashader_1685542983224/work +datashape==0.5.4 +debugpy @ file:///C:/ci_311/debugpy_1676426137692/work +decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work +defusedxml @ file:///tmp/build/80754af9/defusedxml_1615228127516/work +Deprecated==1.2.14 +diff-match-patch @ file:///Users/ktietz/demo/mc3/conda-bld/diff-match-patch_1630511840874/work +dill==0.3.7 +distributed @ file:///C:/b/abs_7509xfv227/croot/distributed_1686866088894/work +Django==4.2.7 +dlib==19.24.1 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file:///C:/b/abs_20ndzb2j6v/croot/importlib-metadata_1678997085534/work +importlib-resources==6.1.0 +imutils==0.5.4 +incremental @ file:///tmp/build/80754af9/incremental_1636629750599/work +inflection==0.5.1 +iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work +intake @ file:///C:/ci_311_rebuilds/intake_1678999914269/work +intervaltree @ file:///Users/ktietz/demo/mc3/conda-bld/intervaltree_1630511889664/work +ipykernel @ file:///C:/ci_311/ipykernel_1678734799670/work +ipython @ file:///C:/b/abs_d1yx5tjhli/croot/ipython_1680701887259/work +ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work +ipywidgets @ file:///C:/b/abs_5awapknmz_/croot/ipywidgets_1679394824767/work +isoduration==20.11.0 +isort @ file:///tmp/build/80754af9/isort_1628603791788/work +itemadapter @ file:///tmp/build/80754af9/itemadapter_1626442940632/work +itemloaders @ file:///opt/conda/conda-bld/itemloaders_1646805235997/work +itsdangerous @ file:///tmp/build/80754af9/itsdangerous_1621432558163/work +jaraco.classes @ file:///tmp/build/80754af9/jaraco.classes_1620983179379/work +jedi @ file:///C:/ci_311/jedi_1679427407646/work +jellyfish @ file:///C:/ci_311/jellyfish_1676450868911/work +Jinja2 @ file:///C:/ci_311/jinja2_1676424968965/work +jinja2-time @ file:///opt/conda/conda-bld/jinja2-time_1649251842261/work +jmespath @ file:///Users/ktietz/demo/mc3/conda-bld/jmespath_1630583964805/work +joblib @ file:///C:/b/abs_1anqjntpan/croot/joblib_1685113317150/work +json5 @ file:///tmp/build/80754af9/json5_1624432770122/work +jsonpatch @ file:///tmp/build/80754af9/jsonpatch_1615747632069/work +jsonpointer==2.1 +jsonschema @ file:///C:/b/abs_d40z05b6r1/croot/jsonschema_1678983446576/work +jstyleson==0.0.2 +jupyter @ file:///C:/ci_311/jupyter_1678249952587/work +jupyter-console @ file:///C:/b/abs_82xaa6i2y4/croot/jupyter_console_1680000189372/work +jupyter-events @ file:///C:/b/abs_4cak_28ewz/croot/jupyter_events_1684268050893/work 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file:///C:/ci_311/python-lsp-black_1678721855627/work +python-lsp-jsonrpc==1.0.0 +python-lsp-server @ file:///C:/b/abs_catecj7fv1/croot/python-lsp-server_1681930405912/work +python-multipart==0.0.6 +python-slugify @ file:///tmp/build/80754af9/python-slugify_1620405669636/work +python-snappy @ file:///C:/ci_311/python-snappy_1676446060182/work +pytoolconfig @ file:///C:/ci_311/pytoolconfig_1678402262175/work +pytz @ file:///C:/ci_311/pytz_1676427070848/work +pyviz-comms @ file:///C:/b/abs_6cq38vhwa5/croot/pyviz_comms_1685030740344/work +PyWavelets @ file:///C:/ci_311/pywavelets_1676504105729/work +pywin32==305.1 +pywin32-ctypes @ file:///C:/ci_311/pywin32-ctypes_1676427747089/work +pywinpty @ file:///C:/ci_311/pywinpty_1677707791185/work/target/wheels/pywinpty-2.0.10-cp311-none-win_amd64.whl +PyYAML @ file:///C:/ci_311/pyyaml_1676432488822/work +pyzmq @ file:///C:/b/abs_655zk4a3s8/croot/pyzmq_1686601465034/work +QDarkStyle @ file:///tmp/build/80754af9/qdarkstyle_1617386714626/work 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file:///C:/ci_311/ruamel_yaml_1676455799258/work +ruamel.yaml @ file:///C:/ci_311/ruamel.yaml_1676439214109/work +s3fs @ file:///C:/b/abs_9ctwhzh1il/croot/s3fs_1680018487962/work +s3transfer @ file:///C:/ci_311/s3transfer_1676434371175/work +sacremoses @ file:///tmp/build/80754af9/sacremoses_1633107328213/work +safetensors==0.4.0 +schedule==1.1.0 +scikit-image @ file:///C:/b/abs_2075zg1pia/croot/scikit-image_1682528361447/work +scikit-learn @ file:///C:/b/abs_38k7ridbgr/croot/scikit-learn_1684954723009/work +scikit-learn-intelex==20230426.121932 +scipy==1.10.1 +Scrapy @ file:///C:/ci_311/scrapy_1678502587780/work +seaborn @ file:///C:/ci_311/seaborn_1676446547861/work +semantic-version==2.10.0 +Send2Trash @ file:///tmp/build/80754af9/send2trash_1632406701022/work +sentencepiece==0.1.99 +service-identity @ file:///Users/ktietz/demo/mc3/conda-bld/service_identity_1629460757137/work +sip @ file:///C:/ci_311/sip_1676427825172/work +six @ file:///tmp/build/80754af9/six_1644875935023/work 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file:///tmp/build/80754af9/sphinxcontrib-serializinghtml_1624451540180/work +spyder @ file:///C:/b/abs_e99kl7d8t0/croot/spyder_1681934304813/work +spyder-kernels @ file:///C:/b/abs_862e4aayi8/croot/spyder-kernels_1681307297029/work +SQLAlchemy @ file:///C:/ci_311/sqlalchemy_1676446707912/work +sqlparse==0.4.4 +stack-data @ file:///opt/conda/conda-bld/stack_data_1646927590127/work +starlette==0.27.0 +statsmodels @ file:///C:/ci_311/statsmodels_1677728225600/work +sympy @ file:///C:/ci_311_rebuilds/sympy_1679009400182/work +tables @ file:///C:/b/abs_52tcd9b9dp/croot/pytables_1685123233455/work +tabulate @ file:///C:/ci_311/tabulate_1676494503192/work +TBB==0.2 +tblib @ file:///Users/ktietz/demo/mc3/conda-bld/tblib_1629402031467/work +tenacity @ file:///C:/b/abs_ddkoa9nju6/croot/tenacity_1682972298929/work +tensorboard==2.15.1 +tensorboard-data-server==0.7.2 +tensorflow==2.15.0 +tensorflow-estimator==2.15.0 +tensorflow-intel==2.15.0 +tensorflow-io-gcs-filesystem==0.31.0 +termcolor==2.4.0 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file:///C:/ci_311/traitlets_1676423290727/work +transformers==4.34.0 +Twisted @ file:///C:/b/abs_f1pc_rieoy/croot/twisted_1683796899561/work +twisted-iocpsupport @ file:///C:/ci_311/twisted-iocpsupport_1676447612160/work +typing_extensions @ file:///C:/b/abs_5em9ekwz24/croot/typing_extensions_1686602003259/work +tzdata==2023.3 +uc-micro-py @ file:///C:/ci_311/uc-micro-py_1676457695423/work +ujson @ file:///C:/ci_311/ujson_1676434714224/work +Unidecode @ file:///tmp/build/80754af9/unidecode_1614712377438/work +uri-template==1.3.0 +urllib3 @ file:///C:/b/abs_889_loyqv4/croot/urllib3_1686163174463/work +uvicorn==0.23.2 +w3lib @ file:///Users/ktietz/demo/mc3/conda-bld/w3lib_1629359764703/work +watchdog @ file:///C:/ci_311/watchdog_1676457923624/work +wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work +webcolors==1.13 +webencodings==0.5.1 +websocket-client @ file:///C:/ci_311/websocket-client_1676426063281/work +websockets==11.0.3 +Werkzeug @ file:///C:/b/abs_8578rs2ra_/croot/werkzeug_1679489759009/work +whatthepatch @ file:///C:/ci_311/whatthepatch_1678402578113/work +widgetsnbextension @ file:///C:/b/abs_882k4_4kdf/croot/widgetsnbextension_1679313880295/work +win-inet-pton @ file:///C:/ci_311/win_inet_pton_1676425458225/work +wrapt @ file:///C:/ci_311/wrapt_1676432805090/work +xarray @ file:///C:/ci_311/xarray_1676447809111/work +xlwings @ file:///C:/ci_311_rebuilds/xlwings_1679013429160/work +xxhash==3.4.1 +xyzservices @ file:///C:/ci_311/xyzservices_1676434829315/work +y-py @ file:///C:/b/abs_b7f5go6r0j/croot/y-py_1683662173571/work +yapf @ file:///tmp/build/80754af9/yapf_1615749224965/work +yarl @ file:///C:/ci_311/yarl_1676432870023/work +ypy-websocket @ file:///C:/b/abs_4e65ywlnv8/croot/ypy-websocket_1684172103529/work +zict @ file:///C:/b/abs_fc7elavmem/croot/zict_1682698759288/work +zipp @ file:///C:/ci_311/zipp_1676426100491/work +zope.interface @ file:///C:/ci_311/zope.interface_1676439868776/work +zstandard==0.19.0 diff --git a/salesprediction.py b/salesprediction.py new file mode 100644 index 00000000..c47138ee --- /dev/null +++ b/salesprediction.py @@ -0,0 +1,97 @@ +import modin.pandas as pd +from sklearn.preprocessing import StandardScaler, LabelEncoder +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +import pickle + +# Load the dataset as an example +df = pd.read_excel('encoded_sample.xlsx') + +X = df.iloc[:, :-1] +y = df.purchase_decision + +# Scale numerical features using StandardScaler +scaler = StandardScaler() +X = scaler.fit_transform(X) + +X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42) + +# Create a Random Forest Classifier using Intel optimized scikit-learn +clf = RandomForestClassifier(n_estimators=100) +# Fit the model on the training data +clf.fit(X_train, y_train) +# Make predictions on the scaled test data +y_pred = clf.predict(X_test) + +# Decode the predictions back to original labels +label_encoder = LabelEncoder() +label_encoder.fit(y) +y_pred_decoded = label_encoder.inverse_transform(y_pred) + +# Evaluate the model +accuracy = accuracy_score(y_test, y_pred_decoded) +print(f"Accuracy: {accuracy}") + +# Save the model to disk +filename = 'finalized_model.sav' +pickle.dump(clf, open(filename, 'wb')) + +# Load the model and make predictions +loaded_model = pickle.load(open(filename, 'rb')) +result = loaded_model.predict(X_test) +accuracy = accuracy_score(y_test, result) +print(f"Accuracy: {accuracy}") + + + + + + + + +# from sklearn.preprocessing import StandardScaler, LabelEncoder +# from sklearn.metrics import accuracy_score +# from sklearn.model_selection import train_test_split +# from sklearn.ensemble import RandomForestClassifier +# import pickle +# import pandas as pd +# from sklearnex import patch_sklearn +# patch_sklearn() + +# # Load the dataset as an example +# df = pd.read_excel('encoded_sample.xlsx') +# X = df.iloc[:, :-1] +# y = df.purchase_decision +# # Scale numerical features using StandardScaler +# scaler = StandardScaler() +# X = scaler.fit_transform(X) +# # y = scaler.transform(y) + + +# X_train, X_test, y_train, y_test = train_test_split( +# X, y, test_size=0.2, random_state=42) + + +# # Create a Random Forest Classifier using Intel optimized scikit-learn +# clf = RandomForestClassifier(n_estimators=100) +# # Fit the model on the training data +# clf.fit(X_train, y_train) +# # Make predictions on the scaled test data +# y_pred = clf.predict(X_test) + +# # Decode the predictions back to original labels +# y_pred_decoded = label_encoder.inverse_transform(y_pred) +# # Evaluate the model +# accuracy = accuracy_score(y_test, y_pred_decoded) +# print(f"Accuracy: {accuracy}") + +# # save the model to disk +# filename = 'finalized_model.sav' +# pickle.dump(clf, open(filename, 'wb')) + +# loaded_model = pickle.load(open(filename, 'rb')) +# result = loaded_model.predict(X_test) +# accuracy = accuracy_score(y_test, y_pred_decoded) +# print(f"Accuracy: {accuracy}") diff --git a/serv.py b/serv.py new file mode 100644 index 00000000..47dcb3d4 --- /dev/null +++ b/serv.py @@ -0,0 +1,77 @@ +# Script A + +import socket +import threading +from transformers import AutoModelForCausalLM, AutoTokenizer +import sys + +def handle_client(client_socket, data_queue): + request = client_socket.recv(1024).decode('utf-8') + print(f"Received request: {request}") + + # Process the request (You can replace this with your actual logic) + response = "ok" + device = "cpu" + + + def find_nth_occurrence(text, target, n): + count = 0 + index = -1 + + while count < n: + index = text.find(target, index + 1) + + if index == -1: + break # Target not found + + count += 1 + + return index + + + target_string = "[/INST]" + nth_occurrence = 2 + + + messages = [ + {"role": "user", "content": "I need some assistance in IT"}, + {"role": "assistant", "content": request[0]}, + {"role": "user", "content": request[1]} + + ] + encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") + # model.save_pretrained() + model_inputs = encodeds.to(device) + model.to(device) + generated_ids = model.generate( + model_inputs, max_new_tokens=1000, do_sample=True) + decoded = tokenizer.batch_decode(generated_ids) + result = find_nth_occurrence(decoded[0], target_string, nth_occurrence) + print(decoded[0][result+7:]) + + # Send the response back to the client + client_socket.send(response.encode('utf-8')) + + # Close the connection + client_socket.close() + + +def start_server(data_queue): + server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + server.bind(('localhost', 8888)) + server.listen(5) + tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") + model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") + print("Server listening on port 8888...") + + while True: + client, addr = server.accept() + print(f"Accepted connection from {addr[0]}:{addr[1]}") + client_handler = threading.Thread( + target=handle_client, args=(client, data_queue)) + client_handler.start() + + +if __name__ == "__main__": + data_queue = [] + start_server(data_queue) diff --git a/shopping-cart.html b/shopping-cart.html new file mode 100644 index 00000000..33cbcf89 --- /dev/null +++ b/shopping-cart.html @@ -0,0 +1,291 @@ +{% load static %} + + + + + + + + + + GenAI | CartList + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + + + + + + + diff --git a/spawn.py b/spawn.py new file mode 100644 index 00000000..fbc89ce7 --- /dev/null +++ b/spawn.py @@ -0,0 +1,45 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer +import sys +statement = sys.argv[1] +prompt2 = sys.argv[2] +clr_st = statement.replace("#", " ") +clr_pmt = prompt2.replace("#", " ") +device = "cpu" + + +def find_nth_occurrence(text, target, n): + count = 0 + index = -1 + + while count < n: + index = text.find(target, index + 1) + + if index == -1: + break # Target not found + + count += 1 + + return index + + +target_string = "[/INST]" +nth_occurrence = 2 + +tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") +model = AutoModelForCausalLM.from_pretrained( + "mistralai/Mistral-7B-Instruct-v0.1") +messages = [ + {"role": "user", "content": "I need some assistance in IT"}, + {"role": "assistant", "content": clr_pmt}, + {"role": "user", "content": clr_st} + +] +encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") +# model.save_pretrained() +model_inputs = encodeds.to(device) +model.to(device) +generated_ids = model.generate( + model_inputs, max_new_tokens=1000, do_sample=True) +decoded = tokenizer.batch_decode(generated_ids) +result = find_nth_occurrence(decoded[0], target_string, nth_occurrence) +print(decoded[0][result+7:]) diff --git a/start.js b/start.js new file mode 100644 index 00000000..bea948f1 --- /dev/null +++ b/start.js @@ -0,0 +1,58 @@ +const express = require('express'); +const bodyParser = require('body-parser'); +const { promisify } = require('util'); +const { execFile } = require('child_process'); + +const app = express(); +const port = 3000; +const cors = require('cors'); +app.use(cors()); + +const script2Path = 'mistral.py'; + + +const execFilePromise = promisify(execFile); + +app.use(bodyParser.json()); +app.use(bodyParser.urlencoded({ extended: false })); + +app.get('/', (req, res) => { + res.send('Initiating the system'); +}); + +app.post('/', async (req, res) => { + let data = req.body.data; + let description = req.body.description; + let botprompt = req.body.botprompt; + description = description.replace(/ /g, "#"); + botprompt = botprompt.replace(/ /g, "#"); + if (data === "mistral") { + try { + const { stdout } = await execFilePromise('/usr/bin/python3', ['mistral.py', description, botprompt]); + console.log('Python process completed successfully'); + res.send({ outputData: stdout }); + } catch (error) { + console.error('Error executing Python process:', error.message); + res.status(500).send({ error: error.message }); + } + } + + if (data === "bert") { + try { + const { stdout } = await execFilePromise('/usr/bin/python3', ['bert1.py', description]); + console.log('Python process completed successfully'); + res.send({ outputData: stdout }); + } catch (error) { + console.error('Error executing Python process:', error.message); + res.status(500).send({ error: error.message }); + } + } + + + + +}); + +app.listen(port, () => { + console.log(`Server is listening on http://localhost:${port}`); +}); diff --git a/test12.html b/test12.html new file mode 100644 index 00000000..a8bc51c2 --- /dev/null +++ b/test12.html @@ -0,0 +1,48 @@ +{% load static %} +

header{{lst}}

+{% for i in lst %} + + + +
+ +
+
+
{{i.0}}
+
$47.00
+
+ + +
+
+ +
+
+ + $ 47.00 + + + +{% endfor %} + + diff --git a/tests.py b/tests.py new file mode 100644 index 00000000..de8bdc00 --- /dev/null +++ b/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/tgg.html b/tgg.html new file mode 100644 index 00000000..07469394 --- /dev/null +++ b/tgg.html @@ -0,0 +1,66 @@ +{% load static %} + + + + + + + + + + + + +
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Hi there! I am here to help you revamp your style
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+ + + + + + + + + + + diff --git a/train.ipynb b/train.ipynb new file mode 100644 index 00000000..86e1c328 --- /dev/null +++ b/train.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7b1ff113-1d95-4f56-bca3-0b7e627f9b74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting torch\n", + " Using cached torch-2.1.1-cp310-cp310-manylinux1_x86_64.whl (670.2 MB)\n", + "Requirement already satisfied: typing-extensions in ./jupyter_env/lib/python3.10/site-packages (from torch) (4.8.0)\n", + "Collecting filelock\n", + " Using cached filelock-3.13.1-py3-none-any.whl (11 kB)\n", + "Requirement already satisfied: sympy in ./jupyter_env/lib/python3.10/site-packages (from torch) (1.12)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in ./jupyter_env/lib/python3.10/site-packages (from torch) (12.1.105)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in ./jupyter_env/lib/python3.10/site-packages (from torch) (12.1.105)\n", + "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in ./jupyter_env/lib/python3.10/site-packages (from torch) (10.3.2.106)\n", + "Collecting triton==2.1.0\n", + " Using cached triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB)\n", + "Collecting fsspec\n", + " Using cached fsspec-2023.12.0-py3-none-any.whl (168 kB)\n", + "Collecting networkx\n", + " Using cached networkx-3.2.1-py3-none-any.whl (1.6 MB)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in ./jupyter_env/lib/python3.10/site-packages (from torch) (2.18.1)\n", + "Collecting nvidia-cublas-cu12==12.1.3.1\n", + " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n", + "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in ./jupyter_env/lib/python3.10/site-packages (from torch) (11.0.2.54)\n", + "Requirement already 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MarkupSafe>=2.0 in ./jupyter_env/lib/python3.10/site-packages (from jinja2->torch) (2.1.3)\n", + "Requirement already satisfied: mpmath>=0.19 in ./jupyter_env/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\n", + "Installing collected packages: nvidia-cusparse-cu12, nvidia-cublas-cu12, networkx, fsspec, filelock, triton, nvidia-cusolver-cu12, nvidia-cudnn-cu12, torch\n", + "Successfully installed filelock-3.13.1 fsspec-2023.12.0 networkx-3.2.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cudnn-cu12-8.9.2.26 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 torch-2.1.1 triton-2.1.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install torch" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6e0881cd-d5be-4226-ad15-b6b436a7ca86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting intel_extension_for_pytorch\n", + " Downloading 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intel_extension_for_pytorch-2.1.0 numpy-1.26.2\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install intel_extension_for_pytorch" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "37c71c49-45a6-4463-b577-61fd25ae156d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install transformers datasets evaluate rouge_score -q" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "187922a3-662c-4ca7-bdca-a28f9e5f615b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting modin\n", + " Downloading modin-0.25.1-py3-none-any.whl (1.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta 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Client()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2823cb75-0dfa-4b42-ae33-292af17e2c88", + "metadata": {}, + "outputs": [], + "source": [ + "import modin.pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "38610962-f245-432a-8644-16b7fbe71dbc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UserWarning: Parallel `read_excel` is a new feature! If you run into any problems, please visit https://github.com/modin-project/modin/issues. If you find a new issue and can't file it on GitHub, please email bug_reports@modin.org.\n" + ] + } + ], + "source": [ + "df = pd.read_excel(\"suggestion_data.xlsx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6139b0e4-a982-4114-8184-ae1878563a35", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset, Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "53c5e167-ebfc-46d1-bdf2-223dd21c0f7c", + "metadata": {}, + "outputs": [], + "source": [ + "data_dict = df.to_dict('list') # convert DataFrame to dictionary of lists\n", + "dataset = Dataset.from_dict(data_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8c670c16-9738-4434-8e13-023d5fbf04ae", + "metadata": {}, + "outputs": [], + "source": [ + "ds = dataset.train_test_split(test_size=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ffa00726-80d0-469b-a352-a2b5922dc04c", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer\n", + "\n", + "checkpoint = \"google/flan-t5-large\"\n", + "tokenizer = AutoTokenizer.from_pretrained(checkpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0eb35036-f62f-43eb-84d6-0ceacd3231ce", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/jupyter_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", + "Token is valid (permission: write).\n", + "Your token has been saved to /home/ubuntu/.cache/huggingface/token\n", + "Login successful\n" + ] + } + ], + "source": [ + "from huggingface_hub import login\n", + "login(\"###########\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "39cfb4fd-69a8-4e8a-a1bb-c62bffd9eb67", + "metadata": {}, + "outputs": [], + "source": [ + "finalDs= fin.train_test_split(test_size=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a9a316d4-0d1e-4585-855b-94c7de590eed", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def preprocess_function(examples):\n", + " inputs = [doc for doc in examples[\"title\"]]\n", + " model_inputs = tokenizer(inputs, max_length=1024, truncation=True)\n", + "\n", + " labels = tokenizer(text_target=examples[\"response\"], max_length=500, truncation=True)\n", + "\n", + " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", + " return model_inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c84f2496-c688-4741-a7eb-ee1be1d9928c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Map: 100%|█████████████████████████| 8373/8373 [00:01<00:00, 5401.60 examples/s]\n", + "Map: 100%|█████████████████████████████| 85/85 [00:00<00:00, 3219.80 examples/s]\n" + ] + } + ], + "source": [ + "tokenized_ds = finalDs.map(preprocess_function, batched=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e2177f0e-f4ab-4ec2-b4b4-3663fff9289a", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import DataCollatorForSeq2Seq\n", + "\n", + "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "4673126c-89d2-456d-a2ed-07e9e3d0f0c0", + "metadata": {}, + "outputs": [], + "source": [ + "import evaluate\n", + "\n", + "rouge = evaluate.load(\"rouge\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b02cad06-8087-4684-81e7-0e4d9d834daa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: transformers in ./jupyter_env/lib/python3.10/site-packages (4.35.2)\n", + "Requirement already satisfied: safetensors>=0.3.1 in ./jupyter_env/lib/python3.10/site-packages (from transformers) (0.4.1)\n", + "Requirement already satisfied: tqdm>=4.27 in ./jupyter_env/lib/python3.10/site-packages (from transformers) (4.66.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in ./jupyter_env/lib/python3.10/site-packages (from transformers) (6.0.1)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in ./jupyter_env/lib/python3.10/site-packages (from transformers) (0.19.4)\n", + "Requirement already satisfied: requests in 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compute_metrics(eval_pred):\n", + " predictions, labels = eval_pred\n", + " decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n", + " labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n", + " decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n", + "\n", + " result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n", + "\n", + " prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n", + " result[\"gen_len\"] = np.mean(prediction_lens)\n", + "\n", + " return {k: round(v, 4) for k, v in result.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b82afd97-b05c-4ae8-b731-2a847d503bc5", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n", + "\n", + "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "2ce5c181-a6ba-4229-9a81-48d5eb11e9c0", + "metadata": {}, + "outputs": [], + "source": [ + "training_args = Seq2SeqTrainingArguments(\n", + " output_dir=\"finalSuggest\",\n", + " evaluation_strategy=\"epoch\",\n", + " learning_rate=1e-4,\n", + " per_device_train_batch_size=16,\n", + " per_device_eval_batch_size=16,\n", + " weight_decay=0.01,\n", + " save_total_limit=1,\n", + " num_train_epochs=5,\n", + " predict_with_generate=True,\n", + " push_to_hub=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "2d5e5b7b-0913-4614-b88c-f55da8d767c3", + "metadata": {}, + "outputs": [], + "source": [ + "trainer = Seq2SeqTrainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=tokenized_ds[\"train\"],\n", + " eval_dataset=tokenized_ds[\"test\"],\n", + " tokenizer=tokenizer,\n", + " data_collator=data_collator,\n", + " compute_metrics=compute_metrics,\n", + " \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "07b69533-1910-4fbf-96fe-b1a2e9902764", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "model.safetensors: 100%|███████████████████| 3.13G/3.13G [01:45<00:00, 29.6MB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "'https://huggingface.co/Bhuvanesh-Ch/finalIPC/tree/main/'" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.push_to_hub()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1acafe9e-69b5-45dc-99b3-a1d32d6e3c0c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/trig.py b/trig.py new file mode 100644 index 00000000..5261d298 --- /dev/null +++ b/trig.py @@ -0,0 +1,37 @@ +import sys +import subprocess + +statement = [sys.argv[1],sys.argv[2]] + + + +def run_script_b(): + # Replace with the actual path to your Script B + script_b_path = 'recieve.py' + + # You can pass command-line arguments to Script B as a list + script_b_arguments = [statement2, statement] + + # Use subprocess to run Script B + process = subprocess.Popen(['/usr/bin/python3', script_b_path] + script_b_arguments, + stdout=subprocess.PIPE, stderr=subprocess.PIPE) + + # Wait for the process to finish and get the return code + return_code = process.wait() + + # Check if Script B was successful + if return_code == 0: + pass + + # print("Script B executed successfully.") + else: + print(f"Script B failed with return code: {return_code}") + + # Capture and print the output of Script B + output, error = process.communicate() + + print(output.decode('utf-8')) + + +if __name__ == "__main__": + run_script_b() diff --git a/urls.py b/urls.py new file mode 100644 index 00000000..93c072cc --- /dev/null +++ b/urls.py @@ -0,0 +1,10 @@ +from django.urls import path + +from . import views + +urlpatterns =[ +path("", views.home, name="home"), +path("casa", views.casa, name="casa"), +path("cart", views.cart, name="cart"), +path("test12", views.test12, name="test12"), +] diff --git a/user_interaction.py b/user_interaction.py new file mode 100644 index 00000000..d2c92124 --- /dev/null +++ b/user_interaction.py @@ -0,0 +1,111 @@ +import cv2 +import mediapipe as mp +import tensorflow as tf # Import Intel Optimized TensorFlow + +# Initialize MediaPipe Hand module +mp_hands = mp.solutions.hands +hands = mp_hands.Hands() + +# Use 0 for default camera, or specify the camera index +cap = cv2.VideoCapture(0) + +# Set the ROI coordinates (adjust these values based on your specific case) +roi_x, roi_y, roi_width, roi_height = 450, 250, 170, 200 + +while True: + ret, frame = cap.read() + + # Convert the BGR image to RGB + rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + # Process the frame and get hand landmarks + results = hands.process(rgb_frame) + + # Check if hands are detected + if results.multi_hand_landmarks: + for hand_landmarks in results.multi_hand_landmarks: + # Check if any landmark is within the ROI + hand_in_roi = all( + roi_x <= lm.x * frame.shape[1] <= roi_x + roi_width and + roi_y <= lm.y * frame.shape[0] <= roi_y + roi_height + for lm in hand_landmarks.landmark + ) + + if hand_in_roi: + # Draw landmarks on the frame + mp_drawing = mp.solutions.drawing_utils + mp_drawing.draw_landmarks( + frame, hand_landmarks, mp_hands.HAND_CONNECTIONS) + + cv2.putText(frame, f"Customer 102 is interacting with the shoe", ( + 10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) + + cv2.imshow('Hand Detection', frame) + + if cv2.waitKey(30) & 0xFF == 27: # Press 'Esc' to exit + break + +cap.release() +cv2.destroyAllWindows() + + + + + + +# import cv2 +# import mediapipe as mp + +# # Initialize MediaPipe Hand module +# mp_hands = mp.solutions.hands +# hands = mp_hands.Hands() + +# # Use 0 for default camera, or specify the camera index +# cap = cv2.VideoCapture(0) + +# # Set the ROI coordinates (adjust these values based on your specific case) +# roi_x, roi_y, roi_width, roi_height = 450, 250, 170, 200 + +# while True: +# ret, frame = cap.read() + +# # Convert the BGR image to RGB +# rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + +# # Process the frame and get hand landmarks +# results = hands.process(rgb_frame) + +# # Check if hands are detected +# if results.multi_hand_landmarks: +# for hand_landmarks in results.multi_hand_landmarks: +# # Check if any landmark is within the ROI +# hand_in_roi = all( +# roi_x <= lm.x * frame.shape[1] <= roi_x + roi_width and +# roi_y <= lm.y * frame.shape[0] <= roi_y + roi_height +# for lm in hand_landmarks.landmark +# ) + +# if hand_in_roi: +# # Draw landmarks on the frame +# mp_drawing = mp.solutions.drawing_utils +# mp_drawing.draw_landmarks( +# frame, hand_landmarks, mp_hands.HAND_CONNECTIONS) + +# cv2.putText(frame, f"Customer 102 is interacting with the shoe", ( +# 10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) + +# # Draw the ROI rectangle on the frame +# # cv2.rectangle(frame, (roi_x, roi_y), (roi_x + roi_width, +# # roi_y + roi_height), (0, 255, 0), 2) +# # cv2.rectangle(frame, (roi_x-250, roi_y), (roi_x + roi_width, +# # roi_y + roi_height), (0, 255, 0), 2) +# # cv2.rectangle(frame, (roi_x-400, roi_y), (roi_x + roi_width, +# # roi_y + roi_height), (0, 255, 0), 2) + +# cv2.imshow('Hand Detection', frame) + +# if cv2.waitKey(30) & 0xFF == 27: # Press 'Esc' to exit +# break + +# cap.release() +# cv2.destroyAllWindows() diff --git a/views.py b/views.py new file mode 100644 index 00000000..539ffccf --- /dev/null +++ b/views.py @@ -0,0 +1,43 @@ +from django.shortcuts import render +from django.http import HttpResponse + +shplist=["T-shirt Contrast Pocket","Basic Flowing Scarf"] +rcmdlist=["black shoe","Basic Flowing Scarf"] + +itemDescription={"T-shirt Contrast Pocket":['$30','static/img/shopping-cart/cart-1.jpg'],"Basic Flowing Scarf":["$47.00","static/img/shopping-cart/cart-3.jpg"], +"black shoe":["$32.50","static/img/shopping-cart/cart-2.jpg"]} +fullDetail=[] +for it in shplist: + currlst=[] + currlst.append(it) + for jk in itemDescription[it]: + currlst.append(jk) + fullDetail.append(currlst) +rcdflist=[] +for it in rcmdlist: + currlst=[] + currlst.append(it) + for jk in itemDescription[it]: + currlst.append(jk) + rcdflist.append(currlst) + +# Create your views here. +def home(request): + return render(request,'index.html') + +def casa(request): + return render(request,'tgg.html') + +def cart(request): + return render(request,'shopping-cart.html',{"lst":fullDetail,"rlst":rcdflist}) + + + + + +# @register.filter +# def get_index(lst, i): +# return lst[i] + +def test12(request): + return render(request,"test12.html",{"lst":shplist,"rlst":rcdflist})