Upload final_app.py

final_app.py

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+import time
+import os
+import json
+import random
+import streamlit as st
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from vectorize_documents import embeddings
+from deep_translator import GoogleTranslator
+from googlesearch import search
+
+# Set up working directory and API configuration
+working_dir = os.path.dirname(os.path.abspath(__file__))
+config_data = json.load(open(f"{working_dir}/config.json"))
+os.environ["GROQ_API_KEY"] = config_data["GROQ_API_KEY"]
+
+def setup_vectorstore():
+    persist_directory = f"{working_dir}/vector_db_dir"
+    vectorstore = Chroma(
+        persist_directory=persist_directory,
+        embedding_function=embeddings
+    )
+    return vectorstore
+
+def chat_chain(vectorstore):
+    from langchain_groq import ChatGroq
+
+    llm = ChatGroq(
+        model="llama-3.1-70b-versatile",
+        temperature=0
+    )
+    retriever = vectorstore.as_retriever()
+    memory = ConversationBufferMemory(
+        llm=llm,
+        output_key="answer",
+        memory_key="chat_history",
+        return_messages=True
+    )
+
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        verbose=True,
+        return_source_documents=True
+    )
+    return chain
+
+def fetch_daily_quote():
+    query = "Bhagavad Gita inspirational quotes"
+    results = list(search(query, num_results=5))  # Convert generator to list
+    if results:
+        return random.choice(results)
+    return "Explore the Bhagavad Gita and Yoga Sutras for timeless wisdom!"
+
+# Streamlit UI
+st.set_page_config(
+    page_title="Bhagavad Gita & Yoga Sutras Assistant",
+    page_icon="🕉️",
+    layout="wide"
+)
+
+st.markdown(
+    """
+    <div style="text-align: center;">
+        <h1 style="color: #4CAF50;">Wisdom Query Assistant</h1>
+        <p style="font-size: 18px;">Explore timeless wisdom with the guidance of a knowledgeable assistant.</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# User name functionality
+if "user_name" not in st.session_state:
+    st.session_state.user_name = ""
+
+if "chat_started" not in st.session_state:
+    st.session_state.chat_started = False
+
+if not st.session_state.chat_started:
+    st.markdown("<h3 style='text-align: center;'>Welcome! Before we begin, please enter your name:</h3>", unsafe_allow_html=True)
+    user_name = st.text_input("Enter your name:", placeholder="Your Name", key="name_input")
+    start_button = st.button("Start Chat")
+
+    if start_button and user_name.strip():
+        st.session_state.user_name = user_name.strip()
+        st.session_state.chat_started = True
+        st.success(f"Hello {st.session_state.user_name}! How can I assist you today?")
+
+# Display the daily quote
+quote = fetch_daily_quote()
+st.markdown(
+    f"""
+    <div style="text-align: center; background-color: #f0f8ff; padding: 10px; border-radius: 5px; margin-bottom: 20px;">
+        <h4>🌟 Daily Wisdom: <a href="{quote}" target="_blank">{quote}</a></h4>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+if st.session_state.chat_started:
+    # Set up vectorstore and chat chain
+    vectorstore = setup_vectorstore()
+    chain = chat_chain(vectorstore)
+
+    # Select language
+    selected_language = st.selectbox("Select your preferred language:", options=[
+        "English", "Hindi", "Bengali", "Telugu", "Marathi", "Tamil", "Urdu", "Gujarati", "Malayalam", "Kannada",
+        "Punjabi", "Odia", "Maithili", "Sanskrit", "Santali", "Kashmiri", "Nepali", "Dogri", "Manipuri", "Bodo",
+        "Sindhi", "Assamese", "Konkani", "Awadhi", "Rajasthani", "Haryanvi", "Bihari", "Chhattisgarhi", "Magahi"
+    ], index=0)
+
+    # Display chat history
+    st.markdown("### 💬 Chat History")
+    if "chat_history" in st.session_state:
+        for chat in st.session_state.chat_history:
+            st.markdown(f"**{st.session_state.user_name}:** {chat['question']}")
+            st.markdown(f"**Assistant:** {chat['answer']}")
+            st.markdown("---")
+
+    # Input box for new query
+    st.markdown(f"### Ask a new question, {st.session_state.user_name}:")
+    with st.form("query_form", clear_on_submit=True):
+        user_query = st.text_input("Your question:", key="query_input", placeholder="Type your query here...")
+        submitted = st.form_submit_button("Submit")
+
+    if submitted and user_query.strip():
+        start_time = time.time()
+        response = chain({"question": user_query.strip()})
+        end_time = time.time()
+
+        answer = response.get("answer", "No answer found.")
+        source_documents = response.get("source_documents", [])
+        execution_time = round(end_time - start_time, 2)
+
+        # Translate response if needed
+        if selected_language != "English":
+            translator = GoogleTranslator(source="en", target=selected_language.lower())
+            translated_answer = translator.translate(answer)
+        else:
+            translated_answer = answer
+
+        # Save chat history
+        if "chat_history" not in st.session_state:
+            st.session_state.chat_history = []
+        st.session_state.chat_history.append({
+            "question": user_query.strip(),
+            "answer": translated_answer
+        })
+
+        # Display source documents if available
+        if source_documents:
+            with st.expander("📜 Source Documents"):
+                for i, doc in enumerate(source_documents):
+                    st.write(f"**Document {i + 1}:** {doc.page_content}")
+
+        st.write(f"**🌟 Enlightened Response:** {translated_answer}")
+        st.write(f"_Response time: {execution_time} seconds_")
+
+    # Sharing options
+    st.markdown(
+        """
+        <div style="text-align: center;">
+            <a href="https://wa.me/?text=Explore%20the%20Bhagavad%20Gita%20%26%20Yoga%20Sutras%20Assistant!%20Check%20it%20out%20here:%20https://your-platform-link" target="_blank">
+                <img src="https://img.icons8.com/color/48/whatsapp.png" alt="WhatsApp" style="margin-right: 10px;">
+            </a>
+            <a href="https://www.linkedin.com/shareArticle?mini=true&url=https://your-platform-link&title=Explore%20Wisdom%20with%20Our%20Assistant" target="_blank">
+                <img src="https://img.icons8.com/color/48/linkedin.png" alt="LinkedIn">
+            </a>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+
+
+
+
+
+
+
+
+
+
+
+# import time
+# import os
+# import json
+# import random
+# import streamlit as st
+# from langchain_huggingface import HuggingFaceEmbeddings
+# from langchain_chroma import Chroma
+# from langchain.memory import ConversationBufferMemory
+# from langchain.chains import ConversationalRetrievalChain
+# from vectorize_documents import embeddings
+# from deep_translator import GoogleTranslator  # For multilingual support
+
+# # Set up working directory and API configuration
+# working_dir = os.path.dirname(os.path.abspath(__file__))
+# config_data = json.load(open(f"{working_dir}/config.json"))
+# os.environ["GROQ_API_KEY"] = config_data["GROQ_API_KEY"]
+
+# def setup_vectorstore():
+#     persist_directory = f"{working_dir}/vector_db_dir"
+#     vectorstore = Chroma(
+#         persist_directory=persist_directory,
+#         embedding_function=embeddings
+#     )
+#     return vectorstore
+
+# def chat_chain(vectorstore):
+#     from langchain_groq import ChatGroq  # Import the LLM class
+
+#     llm = ChatGroq(
+#         model="llama-3.1-70b-versatile",  # Replace with your LLM of choice
+#         temperature=0  # Set low temperature to reduce hallucinations
+#     )
+#     retriever = vectorstore.as_retriever()  # Retrieve relevant chunks
+#     memory = ConversationBufferMemory(
+#         llm=llm,
+#         output_key="answer",
+#         memory_key="chat_history",
+#         return_messages=True
+#     )
+
+#     # Build the conversational retrieval chain
+#     chain = ConversationalRetrievalChain.from_llm(
+#         llm=llm,
+#         retriever=retriever,
+#         chain_type="stuff",  # Define how documents are combined
+#         memory=memory,
+#         verbose=True,
+#         return_source_documents=True
+#     )
+#     return chain
+
+# # Streamlit UI
+# st.set_page_config(
+#     page_title="Bhagavad Gita & Yoga Sutras Assistant",
+#     page_icon="🕉️",  # Custom meaningful favicon
+#     layout="wide"
+# )
+
+# # Title and description with enhanced styling
+# st.markdown(
+#     """
+#     <div style="text-align: center;">
+#         <h1 style="color: #4CAF50;">Wisdom Query Assistant</h1>
+#         <p style="font-size: 18px;">Explore timeless wisdom with the guidance of a knowledgeable assistant.</p>
+#     </div>
+#     """,
+#     unsafe_allow_html=True
+# )
+
+# # Daily Wisdom Quote
+# daily_quotes = [
+#     "You have the right to work, but never to the fruit of work. – Bhagavad Gita",
+#     "Yoga is the journey of the self, through the self, to the self. – Bhagavad Gita",
+#     "When meditation is mastered, the mind is unwavering like the flame of a lamp in a windless place. – Bhagavad Gita",
+#     "Do not dwell in the past, do not dream of the future, concentrate the mind on the present moment. – Buddha",
+# ]
+# st.markdown(
+#     f"""
+#     <div style="text-align: center; background-color: #f0f8ff; padding: 10px; border-radius: 5px; margin-bottom: 20px;">
+#         <h4>🌟 Daily Wisdom: {random.choice(daily_quotes)}</h4>
+#     </div>
+#     """,
+#     unsafe_allow_html=True
+# )
+
+# # Theme Toggle
+# theme = st.radio("Choose a Theme:", options=["Light", "Dark"], index=0, horizontal=True)
+# if theme == "Dark":
+#     st.markdown(
+#         """
+#         <style>
+#         body { background-color: #121212; color: white; }
+#         </style>
+#         """,
+#         unsafe_allow_html=True
+#     )
+
+# vectorstore = setup_vectorstore()
+# chain = chat_chain(vectorstore)
+
+# # Initialize session state
+# if "user_name" not in st.session_state:
+#     st.session_state.user_name = ""
+
+# if "chat_started" not in st.session_state:
+#     st.session_state.chat_started = False
+
+# # Language options
+# languages = [
+#     "English", "Hindi", "Bengali", "Telugu", "Marathi", "Tamil", "Urdu", "Gujarati", "Malayalam", "Kannada",
+#     "Punjabi", "Odia", "Maithili", "Sanskrit", "Santali", "Kashmiri", "Nepali", "Dogri", "Manipuri", "Bodo",
+#     "Sindhi", "Assamese", "Konkani", "Awadhi", "Rajasthani", "Haryanvi", "Bihari", "Chhattisgarhi", "Magahi"
+# ]
+
+# # Input for user name
+# if not st.session_state.chat_started:
+#     st.markdown("<h3 style='text-align: center;'>Welcome! Before we begin, please enter your name:</h3>", unsafe_allow_html=True)
+#     user_name = st.text_input("Enter your name:", placeholder="Your Name", key="name_input")
+#     start_button = st.button("Start Chat")
+
+#     if start_button and user_name.strip():
+#         st.session_state.user_name = user_name.strip()
+#         st.session_state.chat_started = True
+#         st.success(f"Hello {st.session_state.user_name}! How can I assist you today?")
+
+# # Chat functionality
+# if st.session_state.chat_started:
+#     st.markdown(f"<h3 style='text-align: center;'>Hello {st.session_state.user_name}! Ask me anything:</h3>", unsafe_allow_html=True)
+
+#     # Language selection dropdown
+#     selected_language = st.selectbox("Select your preferred language:", options=languages, index=0)
+
+#     # User input and buttons
+#     user_query = st.text_input("💬 Type your question:", placeholder="Type your query here...", key="query_box")
+#     submit_button = st.button("Submit")
+
+#     if submit_button and user_query.strip():
+#         # Generate response
+#         start_time = time.time()
+#         response = chain({"question": user_query.strip()})
+#         end_time = time.time()
+
+#         answer = response.get("answer", "No answer found.")
+#         source_documents = response.get("source_documents", [])
+#         execution_time = round(end_time - start_time, 2)
+
+#         # Translate response
+#         if selected_language != "English":
+#             translator = GoogleTranslator(source="en", target=selected_language.lower())
+#             translated_answer = translator.translate(answer)
+#         else:
+#             translated_answer = answer
+
+#         # Display answer
+#         st.markdown("---")
+#         st.markdown(f"### 🌟 Enlightened Response:")
+#         st.write(translated_answer)
+
+#         # Display source documents
+#         if source_documents:
+#             st.markdown("### 📜 Source Documents:")
+#             for i, doc in enumerate(source_documents):
+#                 with st.expander(f"Source Document {i + 1}"):
+#                     st.write(doc.page_content)
+#         else:
+#             st.markdown("No source documents available.")
+
+#         # Execution time
+#         st.markdown(f"<p style='font-size: 14px;'>Response Time: <strong>{execution_time}</strong> seconds</p>", unsafe_allow_html=True)
+
+#     # Sharing options with icons
+#     st.markdown("---")
+#     st.markdown(
+#         """
+#         <div style="text-align: center;">
+#             <a href="https://wa.me/?text=Explore%20the%20Bhagavad%20Gita%20%26%20Yoga%20Sutras%20Assistant!%20Check%20it%20out%20here:%20https://your-platform-link" target="_blank">
+#                 <img src="https://img.icons8.com/color/48/whatsapp.png" alt="WhatsApp" style="margin-right: 10px;">
+#             </a>
+#             <a href="https://www.linkedin.com/shareArticle?mini=true&url=https://your-platform-link&title=Explore%20Wisdom%20with%20Our%20Assistant" target="_blank">
+#                 <img src="https://img.icons8.com/color/48/linkedin.png" alt="LinkedIn">
+#             </a>
+#         </div>
+#         """,
+#         unsafe_allow_html=True
+#     )
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+# import time
+# import os
+# import json
+# import streamlit as st
+# from langchain_huggingface import HuggingFaceEmbeddings
+# from langchain_chroma import Chroma
+# from langchain.memory import ConversationBufferMemory
+# from langchain.chains import ConversationalRetrievalChain
+# from vectorize_documents import embeddings  # Import embeddings from the vectorization script
+# from deep_translator import GoogleTranslator  # Import Google Translator for multilingual support
+
+# # Set up working directory and API configuration
+# working_dir = os.path.dirname(os.path.abspath(__file__))
+# config_data = json.load(open(f"{working_dir}/config.json"))
+# os.environ["GROQ_API_KEY"] = config_data["GROQ_API_KEY"]
+
+# def setup_vectorstore():
+#     persist_directory = f"{working_dir}/vector_db_dir"
+#     vectorstore = Chroma(
+#         persist_directory=persist_directory,
+#         embedding_function=embeddings
+#     )
+#     return vectorstore
+
+# def chat_chain(vectorstore):
+#     from langchain_groq import ChatGroq  # Import the LLM class
+
+#     llm = ChatGroq(
+#         model="llama-3.1-70b-versatile",  # Replace with your LLM of choice
+#         temperature=0  # Set low temperature to reduce hallucinations
+#     )
+#     retriever = vectorstore.as_retriever()  # Retrieve relevant chunks
+#     memory = ConversationBufferMemory(
+#         llm=llm,
+#         output_key="answer",
+#         memory_key="chat_history",
+#         return_messages=True
+#     )
+
+#     # Build the conversational retrieval chain
+#     chain = ConversationalRetrievalChain.from_llm(
+#         llm=llm,
+#         retriever=retriever,
+#         chain_type="stuff",  # Define how documents are combined
+#         memory=memory,
+#         verbose=True,
+#         return_source_documents=True
+#     )
+#     return chain
+
+# # Streamlit UI
+# st.set_page_config(page_title="Bhagavad Gita & Yoga Sutras Assistant", layout="wide")
+
+# # Title and description with enhanced styling
+# st.markdown(
+#     """
+#     <div style="text-align: center;">
+#         <h1 style="color: #4CAF50;">Wisdom Query Assistant</h1>
+#         <p style="font-size: 18px;">Explore timeless wisdom with the guidance of a knowledgeable assistant.</p>
+#     </div>
+#     """,
+#     unsafe_allow_html=True
+# )
+
+# vectorstore = setup_vectorstore()
+# chain = chat_chain(vectorstore)
+
+# # Initialize session state for user name and chat
+# if "user_name" not in st.session_state:
+#     st.session_state.user_name = ""
+
+# if "chat_started" not in st.session_state:
+#     st.session_state.chat_started = False
+
+# # Language options
+# languages = [
+#     "English", "Hindi", "Bengali", "Telugu", "Marathi", "Tamil", "Urdu", "Gujarati", "Malayalam", "Kannada",
+#     "Punjabi", "Odia", "Maithili", "Sanskrit", "Santali", "Kashmiri", "Nepali", "Dogri", "Manipuri", "Bodo",
+#     "Sindhi", "Assamese", "Konkani", "Awadhi", "Rajasthani", "Haryanvi", "Bihari", "Chhattisgarhi", "Magahi"
+# ]
+
+# # Input for user name
+# if not st.session_state.chat_started:
+#     st.markdown("<h3 style='text-align: center;'>Welcome! Before we begin, please enter your name:</h3>", unsafe_allow_html=True)
+#     user_name = st.text_input("Enter your name:", placeholder="Your Name", key="name_input")
+#     start_button = st.button("Start Chat")
+
+#     if start_button and user_name.strip():
+#         st.session_state.user_name = user_name.strip()
+#         st.session_state.chat_started = True
+#         st.success(f"Hello {st.session_state.user_name}! How can I assist you today?")
+
+# # Chat functionality
+# if st.session_state.chat_started:
+#     st.markdown(f"<h3 style='text-align: center;'>Hello {st.session_state.user_name}! Ask me about Wisdom:</h3>", unsafe_allow_html=True)
+
+#     # Language selection dropdown
+#     selected_language = st.selectbox("Select your preferred language:", options=languages, index=0)
+
+#     # User input and submit button at the bottom
+#     user_query = st.text_input("💬 Your question:", placeholder="Type your query here...", key="query_box")
+#     submit_button = st.button("Submit")
+
+#     if submit_button and user_query.strip():
+#         # Generate response
+#         start_time = time.time()
+#         response = chain({"question": user_query.strip()})
+#         end_time = time.time()
+
+#         answer = response.get("answer", "No answer found.")
+#         source_documents = response.get("source_documents", [])
+#         execution_time = round(end_time - start_time, 2)
+
+#         # Translate the answer based on selected language
+#         if selected_language != "English":
+#             translator = GoogleTranslator(source="en", target=selected_language.lower())
+#             translated_answer = translator.translate(answer)
+#         else:
+#             translated_answer = answer
+
+#         # Display the answer
+#         st.markdown("---")
+#         st.markdown(f"### 🌟 Enlightened Response:")
+#         st.write(translated_answer)
+
+#         # Display source documents
+#         if source_documents:
+#             st.markdown("### 📜 Source Documents:")
+#             for i, doc in enumerate(source_documents):
+#                 with st.expander(f"Source Document {i + 1}"):
+#                     st.write(doc.page_content)
+#         else:
+#             st.markdown("No source documents available.")
+
+#         # Display execution time
+#         st.markdown(f"<p style='font-size: 14px;'>Response Time: <strong>{execution_time}</strong> seconds</p>", unsafe_allow_html=True)
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