app.py

import gradio as gr
import requests
import os
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from ping import add_ping_route


# Configuration
LANGFLOW_API_URL = os.environ.get("LANGFLOW_API_URL", "")
LANGFLOW_API_KEY = os.environ.get("LANGFLOW_API_KEY", "") 
HF_API_KEY = os.environ.get("HF_API_KEY", "") 

if not LANGFLOW_API_URL:
    print("FATAL: LANGFLOW_API_URL secret not found or is empty.")
else:
    print("SUCCESS: LANGFLOW_API_URL loaded securely.")

if not LANGFLOW_API_KEY:
    print("FATAL: LANGFLOW_API_KEY secret not found or is empty.")
else:
    print("SUCCESS: LANGFLOW_API_KEY loaded securely.")

if not HF_API_KEY:
    print("FATAL: HF_API_KEY secret not found or is empty.")
else:
    print("SUCCESS: HF_API_KEY loaded securely.")




# Function that calls the LangFlow chat
def call_langflow(message, history):
    """
    Call Langflow API and return the response
    """
    headers = {
        "Content-Type": "application/json",
    }
    
    # Add API keys
    if HF_API_KEY:
       headers["Authorization"] = f"Bearer {HF_API_KEY}"
    if LANGFLOW_API_KEY: 
        headers["x-api-key"] = f"{LANGFLOW_API_KEY}"
       
    
    # Adjust this payload based on your Langflow API structure
    payload = {
        "input_value": message,
        "output_type": "chat",
        "input_type": "chat",
        "tweaks": {}
    }
    
    try:
        response = requests.post(
            LANGFLOW_API_URL,
            json=payload,
            headers=headers,
            timeout=30
        )
        response.raise_for_status()
        
        # Parse response - adjust based on your API response structure
        data = response.json()
        
        # Common Langflow response structures:
        # Option 1: data["outputs"][0]["outputs"][0]["results"]["message"]["text"]
        # Option 2: data["result"]["message"]
        # Adjust the following line based on your actual response:
        
        bot_message = data["outputs"][0]["outputs"][0]["results"]["message"]["text"]
        return bot_message
        
    except requests.exceptions.RequestException as e:
        return f"Error connecting to Langflow: {str(e)}"
    except (KeyError, IndexError) as e:
        return f"Error parsing response: {str(e)}\nResponse: {data}"

#Create FastAPI App for health check.
fastapi_app = FastAPI(title="Chatbot with Ping API")
# Optional CORS setup
fastapi_app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
# Add /ping route from ping.py 
add_ping_route(fastapi_app, call_langflow)



# Create Gradio Chat Interface with custom CSS
with gr.Blocks(
    css="""
    footer {display: none !important;}
    .footer {display: none !important;}
    #footer {display: none !important;}
    .svelte-1p1dq6v {display: none !important;}
    """
) as demo:
    gr.ChatInterface(
        fn=call_langflow,
        title="CPA Chatbot POC",
        description="You can use this chatbot to answer questions related to Crown Pointe Academy policies.",
        examples=["Summarize the school's uniform policy", "Can a student wear earrings?"],
        retry_btn=None,
        undo_btn=None,
    )

# Mount Gradio app to FastAPI at root path
app = gr.mount_gradio_app(fastapi_app, demo, path="/")