Merge branch 'main' into alo

Author: alo7lika

Algorithms and Deep Learning Models/Beyond-GPS-Navi-Bot/README.md

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+# NaviBot-Voice-Assistant
+
+To get started with NaviBot-Voice-Assistant, follow these steps:
+
+1. Navigate to the project directory:
+
+    ```bash
+    cd NaviBot-Voice-Assistant
+    ```
+
+3. Activating venv (optional) 
+
+    ```bash
+    conda create -n venv python=3.10+
+    conda activate venv
+    ```
+
+4. Install dependencies:
+
+    ```python
+    pip install -r requirements.txt
+    ```
+
+5. Configure environment variables
+    ```
+    Rename `.env-sample` to `.env` file
+    Replace the API your Google API Key, 
+    ```
+    Kindly follow refer to this site for getting [your own key](https://ai.google.dev/tutorials/setup)
+    <br/>
+
+6. Run the chatbot:
+
+    ```bash
+    streamlit run app.py
+    ```

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

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+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()

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

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+import os
+import streamlit as st
+from dotenv import load_dotenv
+import google.generativeai as gen_ai
+import speech_recognition as sr
+from gtts import gTTS
+from tempfile import TemporaryFile
+
+# Load environment variables
+load_dotenv()
+
+# Configure Streamlit page settings
+st.set_page_config(
+    page_title="Chat with Gemini-Pro!",
+    page_icon=":brain:",  # Favicon emoji
+    layout="centered",  # Page layout option
+)
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+# Set up Google Gemini-Pro AI model
+gen_ai.configure(api_key=GOOGLE_API_KEY)
+model = gen_ai.GenerativeModel('gemini-pro')
+
+# Function to translate roles between Gemini-Pro and Streamlit terminology
+def translate_role_for_streamlit(user_role):
+    if user_role == "model":
+        return "assistant"
+    else:
+        return user_role
+
+# Function to recognize speech input
+def recognize_speech():
+    r = sr.Recognizer()
+    with sr.Microphone() as source:
+        st.write("Speak now...")
+        audio = r.listen(source)
+
+    try:
+        user_prompt = r.recognize_google(audio)
+        st.write("You said:", user_prompt)
+        return user_prompt
+    except sr.UnknownValueError:
+        st.write("Sorry, I could not understand your audio.")
+        return ""
+    except sr.RequestError as e:
+        st.write("Could not request results from Google Speech Recognition service; {0}".format(e))
+        return ""
+
+# Function to output voice
+def speak(text):
+    tts = gTTS(text=text, lang='en')
+    with TemporaryFile(suffix=".wav", delete=False) as f:
+        tts.write_to_fp(f)
+        filename = f.name
+    st.audio(filename, format='audio/wav')
+
+
+# Initialize chat session in Streamlit if not already present
+if "chat_session" not in st.session_state:
+    st.session_state.chat_session = model.start_chat(history=[])
+
+# Display the chatbot's title on the page
+st.title("🤖 Gemini Pro - ChatBot")
+
+# Display the chat history
+for message in st.session_state.chat_session.history:
+    with st.chat_message(translate_role_for_streamlit(message.role)):
+        st.markdown(message.parts[0].text)
+
+# Input field for user's message
+voice_input = st.checkbox("Voice Input")
+if voice_input:
+    user_prompt = recognize_speech()
+else:
+    user_prompt = st.text_input("Ask Gemini-Pro...")
+
+if user_prompt:
+    # Add user's message to chat and display it
+    st.chat_message("user").markdown(user_prompt)
+
+    # Send user's message to Gemini-Pro and get the response
+    gemini_response = st.session_state.chat_session.send_message(user_prompt)
+
+    # Display Gemini-Pro's response
+    with st.chat_message("assistant"):
+        st.markdown(gemini_response.text)
+        speak(gemini_response.text)

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

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+import os
+import streamlit as st
+from dotenv import load_dotenv
+import google.generativeai as gen_ai
+import speech_recognition as sr
+from gtts import gTTS
+from tempfile import TemporaryFile
+import webbrowser
+
+# Load environment variables
+load_dotenv()
+
+# Configure Streamlit page settings
+st.set_page_config(
+    page_title="Beyond GPS Navigator!",
+    page_icon=":brain:",  # Favicon emoji
+    layout="centered",  # Page layout option
+)
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+# Set up Google Gemini-Pro AI model
+gen_ai.configure(api_key=GOOGLE_API_KEY)
+model = gen_ai.GenerativeModel('gemini-pro')
+
+# Function to translate roles between Gemini-Pro and Streamlit terminology
+def translate_role_for_streamlit(user_role):
+    if user_role == "model":
+        return "assistant"
+    else:
+        return user_role
+
+# Function to recognize speech input
+def recognize_speech():
+    r = sr.Recognizer()
+    with sr.Microphone() as source:
+        st.write("Speak now...")
+        audio = r.listen(source)
+
+    try:
+        user_prompt = r.recognize_google(audio)
+        st.write("You said:", user_prompt)
+        return user_prompt
+    except sr.UnknownValueError:
+        st.write("Sorry, I could not understand your audio.")
+        return ""
+    except sr.RequestError as e:
+        st.write("Could not request results from Google Speech Recognition service; {0}".format(e))
+        return ""
+
+# Function to output voice
+def speak(text):
+    tts = gTTS(text=text, lang='en')
+    with TemporaryFile(suffix=".wav", delete=False) as f:
+        tts.write_to_fp(f)
+        filename = f.name
+    st.audio(filename, format='audio/wav')
+
+# Get user's current location
+current_location = st.text_input("What is your current location?")
+
+# Ask the user for their destination
+destination = recognize_speech()
+
+# Initialize chat session in Streamlit if not already present
+if "chat_session" not in st.session_state:
+    st.session_state.chat_session = model.start_chat(history=[])
+
+# Display the chatbot's title on the page
+st.title("🤖 Gemini Pro - ChatBot")
+
+# Display the chat history
+for message in st.session_state.chat_session.history:
+    with st.chat_message(translate_role_for_streamlit(message.role)):
+        st.markdown(message.parts[0].text)
+
+# Input field for user's message
+voice_input = st.checkbox("Voice Input")
+if voice_input:
+    user_prompt = recognize_speech()
+else:
+    user_prompt = st.text_input("Ask Gemini-Pro...")
+
+if user_prompt:
+    # Add user's message to chat and display it
+    st.chat_message("user").markdown(user_prompt)
+
+    # Send user's message to Gemini-Pro and get the response
+    gemini_response = st.session_state.chat_session.send_message(user_prompt)
+
+    # Display Gemini-Pro's response
+    with st.chat_message("assistant"):
+        st.markdown(gemini_response.text)
+        speak(gemini_response.text)
+
+    # If the response contains directions, open them in Google Maps
+    if "directions" in gemini_response.text:
+        directions_url = "https://www.google.com/maps/dir/?api=1&origin=" + current_location + "&destination=" + destination
+        webbrowser.open(directions_url)

Algorithms and Deep Learning Models/Beyond-GPS-Navi-Bot/requirements.txt

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+streamlit
+dotenv
+google-generativeai
+speech_recognition
+gtts

Algorithms and Deep Learning Models/Crop Pest and Disease Detection/Dataset/README.md

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+# Dataset
+
+The dataset used in this project is taken from the Kaggle website.
+<br>
+<b>Dataset Link:- https://www.kaggle.com/datasets/nirmalsankalana/crop-pest-and-disease-detection</b>
+<br>
+
+<br>
+This dataset consists of images for detecting various crop pests and diseases. The images are labeled and collected from multiple sources to provide a diverse and comprehensive dataset for training and evaluating machine learning models.
+
+The task is to classify each crop image into one of the predefined categories indicating specific pests or diseases affecting the crops, or to identify healthy crops.
+<br>