Update app.py

app.py

@@ -4,12 +4,6 @@ import pandas as pd
 from langchain.chat_models import ChatOpenAI
 from langchain.document_loaders import CSVLoader
 from langchain_together import TogetherEmbeddings
-from langchain.prompts import ChatPromptTemplate
-from langchain.vectorstores import Chroma
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnableLambda, RunnablePassthrough
-from langchain.document_loaders import CSVLoader
-from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain.vectorstores import Chroma
 from langchain_core.vectorstores import InMemoryVectorStore
 from langchain import PromptTemplate
@@ -17,7 +11,6 @@ from langchain import LLMChain
 from langchain_together import Together
 import os
 
-
 os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c3d72f98ba07d3ed3b5"
 
 # Initialize global variable for vectorstore
@@ -26,7 +19,6 @@ 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"
     payload = {
         "username": "user@123",
@@ -35,25 +27,19 @@ def update_csv_files():
         "client_id": "5",
         "grant_type": "password"
     }
-
-    # Send a POST request to the login URL
     response = requests.post(login_url, data=payload)
 
-    # Check the status and get the response data
     if response.status_code == 200:
         access_token = response.json()['access_token']
     else:
         return f"Failed to log in: {response.status_code}"
 
-    # Profit loss Fetch report
     report_url = "https://livesystem.hisabkarlay.com/connector/api/profit-loss-report"
-    headers = {
-        "Authorization": f"Bearer {access_token}"
-    }
+    headers = {"Authorization": f"Bearer {access_token}"}
     response = requests.get(report_url, headers=headers)
     profit_loss_data = response.json()['data']
     keys = list(profit_loss_data.keys())
-    del keys[23]  # Adjust according to your needs
+    del keys[23]
     del keys[20]
     del keys[19]
     data_dict = {}
@@ -62,7 +48,6 @@ def update_csv_files():
     df = pd.DataFrame(data_dict, index=[0])
     df.to_csv('profit_loss.csv', index=False)
 
-    # API call to get purchase-sell data
     report_url = "https://livesystem.hisabkarlay.com/connector/api/purchase-sell"
     response = requests.get(report_url, headers=headers)
     sell_purchase_data = response.json()
@@ -70,7 +55,6 @@ def update_csv_files():
     df = pd.json_normalize(sell_purchase_data)
     df.to_csv('purchase_sell_report.csv', index=False)
 
-    # API call to get trending product data
     report_url = "https://livesystem.hisabkarlay.com/connector/api/trending-products"
     response = requests.get(report_url, headers=headers)
     trending_product_data = response.json()['data']
@@ -82,22 +66,15 @@ def update_csv_files():
 
 def initialize_embedding():
     global vectorstore
-    # Initialize the embedding function
-
-    # Load CSV files
-    file_paths = [
-        "profit_loss.csv",
-        "purchase_sell_report.csv",
-        "trending_product.csv"
-    ]
+
+    file_paths = ["profit_loss.csv", "purchase_sell_report.csv", "trending_product.csv"]
     documents = []
     for path in file_paths:
         loader = CSVLoader(path, encoding="windows-1252")
-        documents.extend(loader.load())  # Combine documents from all files
+        documents.extend(loader.load())
 
-    # Create an InMemoryVectorStore from the combined documents
     vectorstore = InMemoryVectorStore.from_texts(
-        [doc.page_content for doc in documents],  # Extract the page_content from Document objects
+        [doc.page_content for doc in documents],
         embedding=embeddings,
     )
     return "Embeddings initialized successfully!"
@@ -108,30 +85,18 @@ def qa_chain(query):
     
     retriever = vectorstore.as_retriever()
     retrieved_documents = retriever.invoke(query)
-    return retrieved_documents  # Not shown directly in the UI
+    return retrieved_documents
 
 def generate_response(query, history):
     if vectorstore is None:
-        return history, "Please initialize the embeddings first."
-    
-    retrieved_documents = qa_chain(query)  # Call qa_chain internally
+        return "Please initialize the embeddings first.", history
+
+    retrieved_documents = qa_chain(query)
     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
-    4. Chat with AI Agent
-    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...
+Context: {retrieved_documents}
+Question: {query}
 """
     prompt = PromptTemplate(
         input_variables=['retrieved_documents', 'query'],
@@ -139,33 +104,36 @@ Generate the response according to the user's query:
     )
     
     Generated_chat = LLMChain(llm=llama3, prompt=prompt)
-    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!", placeholder="Type your question here")
-    initialize_status = gr.Textbox(label="Status", visible=False)
-    update_csv_status = gr.Textbox(label="Status", visible=False)
-    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])
+    response = Generated_chat.invoke({'retrieved_documents': retrieved_documents, 'query': query})
+    history.append((query, response['text']))
+    return response['text'], history
+
+def gradio_app():
+    with gr.Blocks() as app:
+        gr.Markdown("# Embedding and QA Interface")
+
+        # Chatbox elements
+        chatbot = gr.Chatbot(label="Chat")
+        query_input = gr.Textbox(label="Enter your query")
+        generate_response_btn = gr.Button("Generate Response")
+
+        # Status output textboxes for CSV update and embedding initialization
+        update_csv_status = gr.Textbox(label="CSV Update Status", interactive=False)
+        initialize_status = gr.Textbox(label="Embedding Initialization Status", interactive=False)
+
+        # Buttons for CSV update and embedding initialization
+        update_csv_button = gr.Button("Update CSV Files")
+        initialize_button = gr.Button("Initialize Embedding")
+
+        # Button click actions
+        update_csv_button.click(update_csv_files, outputs=update_csv_status)
+        initialize_button.click(initialize_embedding, outputs=initialize_status)
 
-    initialize_button.click(initialize_embedding, outputs=initialize_status)
-    update_csv_button.click(update_csv_files, outputs=update_csv_status)
+        # Chatbot functionality with history
+        history = gr.State([])  # Chat history state
+        generate_response_btn.click(generate_response, inputs=[query_input, history], outputs=[chatbot, history])
 
-# Launch Gradio App
-demo.launch()
+    app.launch()
 
+# Run the Gradio app
+gradio_app()