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
@@ -17,15 +11,14 @@ from langchain import LLMChain
 from langchain_together import Together
 import os
 
-
 os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c3d72f98ba07d3ed3b5"
 
 # Initialize global variable for vectorstore
 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"
     payload = {
         "username": "user@123",
@@ -34,26 +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:
-        print("Login successful!")
         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']
@@ -82,23 +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!"
@@ -109,65 +85,16 @@ 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):
+def generate_response(query, history):
     if vectorstore is None:
-        return "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:
-    - 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:
-            -> Profit Loss Report: Detailed analysis of your financial performance.
-            -> Stock Report: Overview of your current inventory status.
-            -> Sales Report: Summary of sales activities.
-            -> Purchase Report: Insights into procurement activities.
-            -> Trending Item Report: Highlights of popular products in demand.
-    - If the user enters only 2, give the following response:
-        For strategic planning and inventory management, consider the following forecasts:
-            -> Sales Forecast: Projected sales for upcoming periods.
-            -> Product Sales Forecast: Expected sales performance of specific products.
-    - If the user enters only 3, give the following response:
-        -> You have expressed interest in identifying the best-selling item. Please allow me to provide you with detailed insights.
-    - If the user enters only 4, give the following response:
-        -> Feel free to ask any questions regarding the status of your business. I’m here to assist you.
-    - If the user enters only 5, give the following response:
-        For inquiries or further assistance, please send your query to:
-            -> Contact Number: +923269498569
-    - If the user enters only 6, give the following response:
-        Your feedback is invaluable to us. Kindly share your thoughts and suggestions at:
-            -> 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
-    - Total Purchase (excluding tax): 3150.00
-    - Purchase Due: 3150.00
-    - Shipping Charges: 0.00
-    - 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}
 """
@@ -178,28 +105,37 @@ Question: {query}
     
     Generated_chat = LLMChain(llm=llama3, prompt=prompt)
     response = Generated_chat.invoke({'retrieved_documents': retrieved_documents, 'query': query})
-    return response['text']
+
+    # Ensure that the history is a list of two-element lists [query, response]
+    history.append([query, response['text']])
+    return response['text'], history
 
 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")
 
+        # Chatbox elements
+        chatbot = gr.Chatbot(label="Chat")
         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)
+
+        # 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)
+
+        # Chatbot functionality with history
+        history = gr.State([])  # Chat history state
+        generate_response_btn.click(generate_response, inputs=[query_input, history], outputs=[chatbot, history])
 
     app.launch()
 
 # Run the Gradio app
-gradio_app()
+gradio_app()