Update app.py

app.py

@@ -1,94 +1,54 @@
-# main.py
-
 import gradio as gr
 from dotenv import load_dotenv
+from langchain_core.messages import HumanMessage, AIMessage
 
 from database.db_handler import init_db
 from langchain_logic.agent_setup import create_agent_executor
 
-import os
+# Load environment variables from .env for local development
+# On Hugging Face, this line will do nothing, which is what we want.
+load_dotenv() 
 
-# --- Load Environment and Initialize ---
-load_dotenv()
+# --- App Setup ---
+# Initialize the database and table if they don't exist
+print("Initializing database...")
 init_db()
-GOOGLE_API_KEY = os.getenv(GOOGLE_API_KEY)
-agent_executor = create_agent_executor()
-
-# --- Initial Bot Message ---
-INITIAL_MESSAGE = """
-Hey! I'm your appointment scheduling assistant. I can help you schedule, find, update, or delete appointments.
+print("Database initialized.")
 
-To schedule an appointment, I'll need the following details:
-- **customer_name**: Customer's full name.
-- **customer_phone**: Customer's contact phone number.
-- **appointment_datetime**: The desired date and time in `YYYY-MM-DD HH:MM:SS` format.
-- **service_type**: The reason for the visit (e.g., "Dental Checkup", "Haircut").
-
-How can I help you today?
-"""
+# Create the agent executor
+agent_executor = create_agent_executor()
+print("Agent Executor created.")
 
+# --- Gradio Interface ---
 
-# --- Gradio Chat Interface Logic ---
-def chat_interface_fn(message, history):
-    """
-    The function that the Gradio ChatInterface will call.
-    It processes the user's message, invokes the agent, and returns the response.
-    """
-    # The 'history' from Gradio is a list of [user, bot] pairs.
-    # We convert it to the list-of-tuples format LangChain's agent expects.
-    chat_history_tuples = []
-    for user_msg, ai_msg in history:
-        # We only want to feed the actual conversational turns to the agent,
-        # not the initial greeting.
-        if ai_msg != INITIAL_MESSAGE:
-            chat_history_tuples.append(("human", user_msg))
-            chat_history_tuples.append(("ai", ai_msg))
+# We need to manage chat history
+def respond(message, chat_history):
+    # Convert Gradio's chat history to LangChain's format
+    history_langchain_format = []
+    for human, ai in chat_history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
 
+    # Invoke the agent
     response = agent_executor.invoke({
         "input": message,
-        "chat_history": chat_history_tuples
+        "chat_history": history_langchain_format
     })
+    
+    # Append the new interaction to the chat history
+    chat_history.append((message, response['output']))
+    return "", chat_history
 
-    # The ChatInterface expects a single string response from the function.
-    # It will automatically append the user's message and this response to the chatbot's history.
-    return response["output"]
 
+# Build the Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Appointment Scheduling Assistant")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your Message", placeholder="Type your request here (e.g., 'show all appointments', 'book a haircut for Jane Doe')")
+    clear = gr.Button("Clear")
 
-def greet():
-    """
-    Function to provide the initial greeting.
-    It must return the data in the format the chatbot expects: a list of [user, bot] pairs.
-    """
-    # FIX: Return the history in the correct format.
-    return [[None, INITIAL_MESSAGE]]
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
 
-
-# --- Launch the Gradio App ---
 if __name__ == "__main__":
-    print("Launching Gradio Appointment Bot...")
-
-    with gr.Blocks(theme=gr.themes.Soft(), title="Appointment Bot") as demo:
-        # Create a Chatbot component that will be populated on load.
-        chatbot = gr.Chatbot(label="Appointment Bot", elem_id="chatbot", height=600)
-
-        # Create the full ChatInterface.
-        chat_interface = gr.ChatInterface(
-            fn=chat_interface_fn,
-            chatbot=chatbot,
-            examples=[
-                ["Find all appointments for 'John Doe'"],
-                ["I'd like to book a 'Financial Consultation' for 2024-12-25 at 14:00:00. My name is Jane Doe and my number is 555-876-5432."],
-                ["Please delete all 'Maintenance Check' appointments."],
-                ["Update the phone number for Jane Doe to 555-999-0000"]
-            ],
-            title="Conversational Appointment Scheduler"
-        )
-
-        # Use a 'load' event to populate the initial greeting using the greet function.
-        demo.load(
-            fn=greet,
-            inputs=None,
-            outputs=chatbot
-        )
-
-    demo.launch()
+    demo.launch(debug=True) # debug=True is for local testing