Nifty50 index price prediction added in prediction models

Author: deepanshubaghel

Algorithms and Deep Learning Models/Beyond-GPS-Navi-Bot/app.py

@@ -1,133 +1,133 @@
-import streamlit as st
-import pyttsx3
-import speech_recognition as sr
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain_community.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-
-load_dotenv()
-os.getenv("GOOGLE_API_KEY")
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-# Initialize pyttsx3 for voice output
-engine = pyttsx3.init()
-
-# Function to speak the text
-def speak(text):
-    engine.say(text)
-    engine.runAndWait()
-
-# Function to listen to voice input
-def listen():
-    r = sr.Recognizer()
-    with sr.Microphone() as source:
-        st.write("Listening...")
-        r.adjust_for_ambient_noise(source)
-        audio = r.listen(source)
-    
-    try:
-        user_input = r.recognize_google(audio)
-        st.write(f"You said: {user_input}")
-        return user_input
-    except sr.UnknownValueError:
-        st.write("Sorry, I could not understand what you said.")
-        return None
-    except sr.RequestError as e:
-        st.write(f"Could not request results from Google Speech Recognition service; {e}")
-        return None
-
-
-def get_pdf_text(pdf_docs):
-    text=""
-    for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-
-def get_conversational_chain():
-
-    prompt_template = """
-    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-
-    model = ChatGoogleGenerativeAI(model="gemini-pro",
-                             temperature=0.3)
-
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-    return chain
-
-
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-    # Load the local FAISS index with dangerous deserialization allowed
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    response = chain(
-        {"input_documents": docs, "question": user_question},
-        return_only_outputs=True
-    )
-
-    speak(response["output_text"])  # Speak the response
-    st.write("Reply: ", response["output_text"])
-
-
-def main():
-    st.set_page_config("Beyond GPS Navigation")
-    st.header("Beyond GPS Navigator for Blind")
-
-    user_question = st.text_input("Ask your query")
-    voice_input_button = st.button("Voice Input")
-
-    if voice_input_button:
-        user_question = listen()  # Listen to voice input
-        if user_question:
-            user_input(user_question)
-
-    if user_question:
-        user_input(user_question)
-
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your route data and Click on the Submit & Process Button", accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import pyttsx3
+import speech_recognition as sr
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain_community.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+load_dotenv()
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Initialize pyttsx3 for voice output
+engine = pyttsx3.init()
+
+# Function to speak the text
+def speak(text):
+    engine.say(text)
+    engine.runAndWait()
+
+# Function to listen to voice input
+def listen():
+    r = sr.Recognizer()
+    with sr.Microphone() as source:
+        st.write("Listening...")
+        r.adjust_for_ambient_noise(source)
+        audio = r.listen(source)
+    
+    try:
+        user_input = r.recognize_google(audio)
+        st.write(f"You said: {user_input}")
+        return user_input
+    except sr.UnknownValueError:
+        st.write("Sorry, I could not understand what you said.")
+        return None
+    except sr.RequestError as e:
+        st.write(f"Could not request results from Google Speech Recognition service; {e}")
+        return None
+
+
+def get_pdf_text(pdf_docs):
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text+= page.extract_text()
+    return  text
+
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+
+def get_conversational_chain():
+
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = ChatGoogleGenerativeAI(model="gemini-pro",
+                             temperature=0.3)
+
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+    # Load the local FAISS index with dangerous deserialization allowed
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+
+    speak(response["output_text"])  # Speak the response
+    st.write("Reply: ", response["output_text"])
+
+
+def main():
+    st.set_page_config("Beyond GPS Navigation")
+    st.header("Beyond GPS Navigator for Blind")
+
+    user_question = st.text_input("Ask your query")
+    voice_input_button = st.button("Voice Input")
+
+    if voice_input_button:
+        user_question = listen()  # Listen to voice input
+        if user_question:
+            user_input(user_question)
+
+    if user_question:
+        user_input(user_question)
+
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your route data and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+
+
+if __name__ == "__main__":
+    main()