Update app.py

app.py

@@ -24,6 +24,7 @@ os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c
 vectorstore = None
 embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
 llama3 = Together(model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", max_tokens=1024)
+
 def update_csv_files():
     # Define the login URL and credentials
     login_url = "https://livesystem.hisabkarlay.com/auth/login"
@@ -111,19 +112,16 @@ def qa_chain(query):
     retrieved_documents = retriever.invoke(query)
     return retrieved_documents  # Not shown directly in the UI
 
-def generate_response(query):
+def generate_response(query, history):
     if vectorstore is None:
-        return "Please initialize the embeddings first."
+        return history, "Please initialize the embeddings first."
     
     retrieved_documents = qa_chain(query)  # Call qa_chain internally
     chat_template = """
 You are a highly intelligent and professional AI assistant.
-
 Generate the response according to the user's query:
-
 - If the user enters a greeting (e.g., "Hi", "Hello", "Good day"), give the following response:
     "Welcome to HisabKarLay, your business partner! You may choose from the following services 👇:
-
     1. Reports
     2. Forecasts
     3. Best Selling Items
@@ -131,15 +129,10 @@ Generate the response according to the user's query:
     5. Chat with our Customer Care Team
     6. Share your Feedback
     7. Checkout Latest Offers
-
     🔆 Suggestion: To make a selection, send the relevant number like 1
-
     ⭕ Note: If at any stage you wish to go back to the previous menu, type back, and to go to the main menu, type main menu.
-
     ⭕ Note: If you want to change the language, type and send 'change language.'
-
     💁🏻♂️ Help: If you need any help, you can call us at +923269498569."
-
 - If the user enters a specific number (1-7), give the following responses:
     - If the user enters only 1, give the following response:
         If you are interested in insights related to your business, please find the available reports below:
@@ -164,11 +157,8 @@ Generate the response according to the user's query:
             -> Contact Number: +923269498569
     - If the user enters only 7, give the following response:
         -> Check out our latest offers and promotions to maximize your business potential.
-
 - **Fallback**: If the query doesn't match a greeting or a specific command (1-7), provide a professional and clean response based on the user's question.
-
     When answering based on retrieved documents, make sure to exclude unnecessary metadata (like document IDs) and display only the relevant content. For example, extract the actual report details such as sales, purchases, and other key information without showing raw document metadata.
-
     Example response for a "purchase report":
     "It seems like you're asking about the purchase report. Here's what I found:
     - Total Purchase (including tax): 3150.00
@@ -178,9 +168,7 @@ Generate the response according to the user's query:
     - Additional Expenses: 0.00
     - Sales Total (including tax): 1000000000000953067.33
     - Sales Total (excluding tax): 1000000000000945928.50"
-
     Ensure the information is formatted clearly and no irrelevant document information (such as IDs or metadata) is displayed.
-
 Context: {retrieved_documents}
 Question: {query}
 """
@@ -190,29 +178,31 @@ Question: {query}
     )
     
     Generated_chat = LLMChain(llm=llama3, prompt=prompt)
-    response = Generated_chat.invoke({'retrieved_documents': retrieved_documents, 'query': query})
-    return response['text']
-
-def gradio_app():
-    with gr.Blocks() as app:
-        gr.Markdown("# Embedding and QA Interface")
-        
-        update_btn = gr.Button("Update CSV Files")
-        update_output = gr.Textbox(label="Update Output")
-
-        initialize_btn = gr.Button("Initialize Embedding")
-        initialize_output = gr.Textbox(label="Output")
-
-        query_input = gr.Textbox(label="Enter your query")
-        generate_response_btn = gr.Button("Generate Response")
-        response_output = gr.Textbox(label="Generated Response")
-        
-        # Button actions
-        update_btn.click(update_csv_files, outputs=update_output)
-        initialize_btn.click(initialize_embedding, outputs=initialize_output)
-        generate_response_btn.click(generate_response, inputs=query_input, outputs=response_output)
-
-    app.launch()
-
-# Run the Gradio app
-gradio_app()
+    result = Generated_chat.run({
+        "retrieved_documents": retrieved_documents,
+        "query": query
+    })
+
+    # Append the conversation history
+    history.append((query, result))
+
+    return history, result
+
+# Define Gradio UI
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(label="AI Chat")
+    query = gr.Textbox(label="Ask anything!")
+    initialize_button = gr.Button("Initialize Embeddings")
+    update_csv_button = gr.Button("Update CSV Files")
+
+    def on_query(query, history):
+        return generate_response(query, history)
+    
+    query.submit(on_query, [query, chatbot], [chatbot, query])
+
+    initialize_button.click(initialize_embedding, outputs=None)
+    update_csv_button.click(update_csv_files, outputs=None)
+
+# Launch Gradio App
+demo.launch()
+