Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import streamlit as st
 import json
 import os 
@@ -46,16 +45,16 @@ database_schema = db.get_table_info()
 #=================================Logging=====================================#
 
 
-# log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-# log_folder = log_file.parent
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
 
-# scheduler = CommitScheduler(
-#     repo_id="E-commerce-logs",
-#     repo_type="dataset",
-#     folder_path=log_folder,
-#     path_in_repo="data",
-#     every=2
-# )
+scheduler = CommitScheduler(
+    repo_id="chatbot-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=1
+)
 
 
 #=================================SQL_AGENT=====================================#
@@ -69,10 +68,8 @@ You must query only the columns that are needed to answer the question. Wrap eac
 You have access to tools for interacting with the database.
 Only use the given tools. Only use the information returned by the tools to construct your final answer.
 You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.
-
 DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
 You are not allowed to make dummy data.
-
 If the question does not seem related to the database, just return "I don't know" as the answer.
 Before you execute the query, tell us why you are executing it and what you expect to find briefly.
 Only use the following tables:
@@ -111,10 +108,8 @@ sqlite_agent = create_sql_agent(
 def sql_tool(user_input):
     """
     Executes a SQL query using the sqlite_agent and returns the result.
-
     Args:
         user_input (str): a natural language query string explaining what information is required while also providing the necessary details to get the information.
-
     Returns:
         str: The result of the SQL query execution. If an error occurs, the exception is returned as a string.
     """
@@ -145,18 +140,31 @@ def log_action(task, details):
         "task": [task],
         "details": [details]
     })], ignore_index=True)
-    print(f"{task} success")
+
+    # Save the log to the file
+    with scheduler.lock:
+        # Ensure the log folder exists
+        log_folder.mkdir(parents=True, exist_ok=True)
+        
+        # Open the log file in append mode
+        with log_file.open("a") as f:
+            f.write(json.dumps({
+                "timestamp": datetime.now().isoformat(),
+                "task": task,
+                "details": details
+            }))
+            f.write("\n")
+
+    #print(f"{task} logged successfully.")
 
 # Define dummy tools
 @tool
 def cancel_order(order_id, customer_id):
     """
     Logs the action of canceling an order.
-
     Args:
         order_id (int): The unique ID of the order to be canceled.
         customer_id (int): The unique ID of the customer requesting the cancellation.
-
     Returns:
         None: The function logs the action and does not return any value.
     """
@@ -172,12 +180,10 @@ def cancel_order(order_id, customer_id):
 def process_refund(order_id, refund_amount, reason):
     """
     Logs the action of processing a refund for an order.
-
     Args:
         order_id (int): The unique ID of the order for which the refund is processed.
         refund_amount (float): The amount to be refunded.
         reason (str): The reason for initiating the refund.
-
     Returns:
         None: The function logs the action and does not return any value.
     """
@@ -194,11 +200,9 @@ def process_refund(order_id, refund_amount, reason):
 def change_address(order_id, new_address):
     """
     Logs the action of changing the shipping address for an order.
-
     Args:
         order_id (int): The unique ID of the order for which the address is being changed.
         new_address (str): The new shipping address to be updated.
-
     Returns:
         None: The function logs the action and does not return any value.
     """
@@ -209,32 +213,26 @@ def change_address(order_id, new_address):
     }
     log_action("change_address", details)
 
-
-feedback_log = pd.DataFrame(columns=["timestamp", "intent", "customer_id", "feedback", "rating"])
-
 @tool
 def register_feedback(intent, customer_id, feedback, rating):
     """
     Logs customer feedback into the feedback log.
-
     Args:
         intent (str): The category of the support query (e.g., "cancel_order", "get_refund").
         customer_id (int): The unique ID of the customer.
         feedback (str): The feedback provided by the customer.
         rating(int): The rating provided by the customer out of 5
-
     Returns:
         str: Success message.
     """
-    global feedback_log
-    feedback_entry = {
+    details = {
         "timestamp": datetime.now(),
         "intent": intent,
         "customer_id": customer_id,
         "feedback": feedback,
         "rating": rating
     }
-    feedback_log = pd.concat([feedback_log, pd.DataFrame([feedback_entry])], ignore_index=True)
+    log_action("register_feedback", details)
     print("register_feedback success")
     return "Feedback registered successfully!"
 
@@ -244,12 +242,10 @@ deferred_cases = pd.DataFrame(columns=["timestamp", "customer_id", "query", "rea
 def defer_to_human(customer_id, query, intent, reason):
     """
     Logs customer details and the reason for deferring to a human agent.
-
     Args:
         customer_id (int): The unique ID of the customer whose query is being deferred.
         query (str): The customer's query or issue that needs human intervention.
         reason (str): The reason why the query cannot be resolved by the chatbot.
-
     Returns:
         str: Success message indicating the deferral was logged.
     """
@@ -270,7 +266,6 @@ def defer_to_human(customer_id, query, intent, reason):
 def days_since(delivered_date):
     """
     Calculates the number of days since the product was delivered.
-
     Args:
         delivered_date (str): The date when the product was delivered in the format 'YYYY-MM-DD'.
     """
@@ -290,32 +285,23 @@ def days_since(delivered_date):
 #=================================CHATBOT====================================#
 system_message = f"""
 Here’s a comprehensive system prompt for your e-commerce chatbot with clear instructions on how the chatbot should use the tools, follow the specified workflows, and handle cases outside its scope.
-
 ---
-
 ### **System Prompt**
-
 You are an intelligent e-commerce chatbot designed to assist users with post-order queries. Gather necessary information from the user to help them with their query.
 Use the following workflow to solve user queries. do not provide sql inputs to the sql tool - you only need to ask in natural language what information you need.
   - If at any point you cannot determine the next steps - defer to human. you do not have clearance to go beyond the scope the following flow.
-
 ### **Workflow:**
-
 #### 1. **Cancel Order**
 - Get necessary details and why customer wants to cancel.
 - Retrieve cancellation fee, validity period, and amount for the product with `sql_tool`.
 - Check If the cancellation meets the criteria - proceed to "Get Refund" flow.
-
 #### 2. **Check Cancellation Fee**
 - Check the cancellation fee using `sql_tool`.
-
 #### 3. **Check Refund Policy**
 - Provide the refund policy details using `sql_tool`.
-
 #### 4. **Get Invoice**
 - Retrieve necessary details using `sql_tool`.
 - Provide the invoice information to the customer.
-
 #### 5. **Get Refund**
 - Retrieve necessary details using `sql_tool`.
 - Retrieve cancellation fee, validity period, and amount for the product with `sql_tool`.
@@ -327,16 +313,13 @@ Use the following workflow to solve user queries. do not provide sql inputs to t
   - Process a refund using `process_refund` (output as JSON). refund amount should be product price - cancellation fee.
   if it doesn't meet the criteria you do not have the clearance to process refund. defer to human.
 - If at any point you cannot determine the next steps - defer to human.
-
 #### 6. **Track Order/Track Refund**
 - Get necessary information from user
 - Retrieve the order or refund status using `sql_tool`.
 - Provide the information to the customer.
-
 #### 7. **Change Shipping Address**
 - Retrieve necessary details using `sql_tool`.
 - Update the shipping address with `change_address`.
-
 MANDATORY STEP:
 After helping the customer with their concern,
 - Ask if the customer needs help with anything else. If they ask for anything from the above list help them.
@@ -348,16 +331,12 @@ Once the customer confirms they do not need any further assistance:
    - The feedback received.
    - the rating given
 ---
-
 ### **Handling Out-of-Scope Queries:**
 If the user's query, at any point is not covered by the workflows above:
 - Respond:
   > "This is beyond my skill. Let me connect you to a customer service agent" and get necessary details from the customer and use the defer_to_human tool.
 - End the conversation.
-
-
 ---
-
 ### **Important Notes for the Model:**
 - Always aim to minimize the number of questions asked by retrieving as much information as possible from the database using `sql_tool` - retrieve customer information using any details provided and use it throughout the conversation.
 - Follow the exact workflows for each query category.
@@ -422,4 +401,4 @@ def chat_with_agent():
             st.markdown(chatbot_response)
 
 if __name__ == "__main__":
-    chat_with_agent()
+    chat_with_agent()