Create app.py

app.py

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+import os
+import gradio as gr
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+
+# ✅ Load OpenAI API Key
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+
+# ✅ Initialize OpenAI Model with LangChain
+model = ChatOpenAI(
+    model="gpt-4o-mini", 
+    openai_api_key=api_key
+)
+
+# ✅ Initialize HuggingFace Embeddings
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# ✅ Initialize Chroma Vector Store (No need for chromadb.PersistentClient)
+vector_store = Chroma(
+    collection_name="chat_collection",  # Specify the collection name
+    embedding_function=embeddings,
+    persist_directory="./chroma_langchain_db",  # Directory to store data locally
+)
+
+# ✅ Step 1: Helper Functions for Chat Memory
+def get_chat_history(user_id):
+    """Fetches stored messages for a given user from the vector store."""
+    # Use Chroma's retriever to fetch documents filtered by user_id
+    retriever = vector_store.as_retriever(
+        search_type="mmr",  # Modify search type as needed (e.g., "mmr" for Maximal Marginal Relevance)
+        search_kwargs={"k": 100, "fetch_k": 100}  # Adjust the number of results to fetch and how many to filter
+    )
+    
+    # Use the retriever to fetch results, filtered by the user_id
+    results = retriever.invoke("Chat history", filter={"user_id": user_id})
+    # Extract the page content (chat messages) from the results
+    user_history = [doc.page_content for doc in results]
+    return "\n".join(user_history) if user_history else ""
+
+def store_chat_message(user_id, user_input, bot_response):
+    """Stores user-bot conversations in ChromaDB."""
+    chat_entry = f"User: {user_input}\nBot: {bot_response}"
+    # Add to vector store with user_id as metadata
+    vector_store.add_documents([Document(page_content=chat_entry, metadata={"user_id": user_id})])
+
+# ✅ Step 2: Generate Response Using OpenAI GPT
+def generate_response(username, user_input):
+    """Generates a chatbot response using GPT-4 and stores chat history."""
+    user_id = username.lower().strip()
+    history = get_chat_history(user_id)
+    # Append the latest user input to the conversation history
+    messages = [{"role": "system", "content": "You are a helpful AI assistant. Please provide answer in 20 words only"}]
+    if history:
+        messages.append({"role": "user", "content": f"Chat history:\n{history}"})
+    # Include the latest user input in the conversation
+    messages.append({"role": "user", "content": user_input})
+    print(messages)
+    response = model.invoke(messages)  # Using the model to generate response
+    bot_response = response.content
+    # Store the conversation for future reference
+    store_chat_message(user_id, user_input, bot_response)
+    # Return the entire conversation including the user's input and bot's response
+    return f"{history}\nUser: {user_input}\nBot: {bot_response}"
+
+# ✅ Step 3: Gradio UI with User Dropdown
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔥 Multi-User Chatbot with GPT-4 and Memory (ChromaDB)")
+
+    # Dropdown for selecting user
+    username_input = gr.Dropdown(
+        label="Select User",
+        choices=["Aarya", "Ved", "Vivaan"],
+    )
+
+    # Chat input and output
+    chat_input = gr.Textbox(label="Your Message", placeholder="Type here...")
+    chat_output = gr.Textbox(label="Chatbot Response", interactive=False)
+
+    # Button to send the message
+    chat_button = gr.Button("Send")
+    chat_button.click(generate_response, inputs=[username_input, chat_input], outputs=chat_output)
+
+# ✅ Step 4: Run the Gradio app
+demo.launch()