Add app.py and requirements.txt, update README

README.md

@@ -1,37 +1,19 @@
+# File: README.md
+
 ---
-title: Simple Echo Agent Demo
-emoji: 👋
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 4.31.0 # Or your Gradio version
-app_file: app.py
+title: "GHAPA: Intelligent Health AI Assistant"
+emoji: "🧠⚡"
+colorFrom: "blue"
+colorTo: "green"
+sdk: "gradio"
+app_file: "app.py"
 pinned: false
-license: apache-2.0
-tags:
-  - agent-demo-track # IMPORTANT for Track 3
-  - gradio
-  - hackathon
+# This app runs on the free CPU tier, as all heavy work is done by Modal
+secrets:
+- MODAL_BACKEND_URL
 ---
 
-# Simple Echo Agent Demo
-
-**Hackathon Track:** Agentic Demo Showcase (`agent-demo-track`)
-
-## Description
-
-This is a very simple Gradio app for the Agents-MCP-Hackathon.
-It demonstrates a basic "agent" that just echoes back whatever you type, with a prefix.
-
-## How to Use
-
-1.  Type a message in the input box.
-2.  Click the "Echo Message" button.
-3.  See the agent's response.
-
-## Video Overview
-
-*   **[YOUR VIDEO LINK HERE]** - *You MUST create a short video showing your app and link it here.*
-    *   _Example: A screen recording of you typing and seeing the echo._
+# 🧠 GHAPA: Intelligent Health AI Assistant (Powered by Modal ⚡)
+This is the user interface for the GHAPA system. It provides a simple, responsive UI that communicates with a powerful, GPU-accelerated backend hosted on Modal Labs.
 
----
+Ask your health question in **English, Spanish, Hindi, or French**. The system will auto-detect your language, find the most relevant PubMed article, and provide a trusted answer at high speed.

app.py

@@ -1,55 +1,62 @@
+# File: app.py
+
 import gradio as gr
-import time # For a more "natural" typing simulation
+import requests
+import os
+
+# --- Configuration ---
+# This securely retrieves the backend URL from the Hugging Face Space secret.
+MODAL_BACKEND_URL = os.getenv("MODAL_BACKEND_URL")
+
+# A critical check to ensure the secret is set. The app will not start without it.
+if not MODAL_BACKEND_URL:
+    raise ValueError("The MODAL_BACKEND_URL secret is not set in your Hugging Face Space! Please add it in the Settings tab.")
 
-def echo_chat_function(message, history):
+# This function is the bridge between your Gradio UI and your Modal backend.
+def call_modal_backend(user_input: str):
     """
-    Responds to a user's message in a chat context.
-    'message' is the latest user input.
-    'history' is a list of previous [user_message, bot_message] pairs.
+    Takes the user's input, sends it to the Modal backend via an HTTP POST
+    request, and returns the final markdown response.
     """
-    if not message:
-        # In a chat, if the user sends an empty message,
-        # you might want to prompt them or do nothing.
-        return "Agent: Hmm, did you mean to send something?"
-
-    # This is where you would put your agent's logic (e.g., LLM calls).
-    # For now, we'll just echo with a prefix.
-    response_prefix = "Agent Echo: "
-    full_response = f"{response_prefix}{message}"
-
-    # Simulate the agent "typing" for a better chat feel
-    # gr.ChatInterface handles streaming if you yield partial responses
-    partial_response = ""
-    for char in full_response:
-        partial_response += char
-        time.sleep(0.03)  # Small delay for each character
-        yield partial_response # Yield intermediate parts of the response
-
-# Create the Gradio Chat Interface
-chat_iface = gr.ChatInterface(
-    fn=echo_chat_function,             # The Python function to call for responses
-    chatbot=gr.Chatbot(
-        height=400,
-        # The deprecation warning you saw earlier is for the underlying data format.
-        # When using ChatInterface, it generally handles this for you.
-        # If you were manually populating a Chatbot, you'd use:
-        # value=[{"role": "user", "content": "Hi"}], type="messages"
-        # But ChatInterface handles this internally.
-    ),
-    textbox=gr.Textbox(placeholder="Type your message here and press Enter...",
-                       container=False, scale=7), # Input textbox configuration
-    title="Simple Echo Chat Agent ",
-    description="""
-    This is a simple chat interface for the Agents-MCP-Hackathon.
-    Type a message, and the 'agent' will echo it back.
-    This version includes specific button configurations suitable for newer Gradio versions.
-    """,
-    examples=[["Hello!"], ["How are you today?"]], # Example prompts
-    cache_examples=False, # Don't cache examples for dynamic functions usually
+    if not user_input or not user_input.strip():
+        return "Please enter a question to get started."
+
+    # Prepare the request headers and payload
+    headers = {"Content-Type": "application/json"}
+    payload = {"user_input": user_input}
+
+    try:
+        # Make the POST request to the Modal serverless function
+        response = requests.post(MODAL_BACKEND_URL, headers=headers, json=payload, timeout=300)
+        
+        # Raise an exception for bad HTTP status codes (like 404 or 500)
+        response.raise_for_status()
+        
+        # Parse the JSON response from the backend
+        result = response.json()
+        
+        # Safely get the final markdown. If the key is missing, return an error.
+        return result.get("final_markdown", "### ❗ Error\nReceived an unexpected response format from the backend.")
+
+    except requests.exceptions.Timeout:
+        return "### ❗ Error: The request timed out. The AI backend may be busy or starting up. Please try again in a moment."
+    except requests.exceptions.RequestException as e:
+        # Handle any other network errors (e.g., connection failed)
+        return f"### ❗ Error: Could not connect to the AI backend.\n**Details**: {e}"
+
+# --- Gradio User Interface ---
+with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
+    gr.Markdown("""# 🧠 GHAPA: Intelligent Health AI Assistant (Powered by Modal ⚡)
+Ask your health question in **English, Spanish, Hindi, or French**. The system fetches trusted PubMed answers at high speed.""")
+
+    with gr.Row():
+        inp = gr.Textbox(label="Enter your question", placeholder="e.g., 'What are the symptoms of dengue?'", scale=4)
+        btn = gr.Button("Submit", variant="primary", scale=1)
     
-    submit_btn="Send"               # Text for the submit button (optional, Enter also works)
-)
+    o5 = gr.Markdown(label="Answer & Explanation")
+
+    # The click function is now simple and calls our backend function.
+    btn.click(fn=call_modal_backend, inputs=[inp], outputs=[o5])
 
-# Launch the app
 if __name__ == "__main__":
-    chat_iface.launch()
+    demo.launch()

requirements.txt

@@ -1 +1,4 @@
-gradio
+# File: requirements.txt
+
+gradio==4.44.1
+requests