import gradio as gr
import requests
import os

# Configuration
LANGFLOW_API_URL = os.environ.get("LANGFLOW_API_URL", "https://rossiter78-langflowpoc.hf.space/api/v1/run/d53e1b2f-3572-40c8-84ab-725106ee858f")
#LANGFLOW_API_URL = "https://rossiter78-langflowpoc.hf.space/api/v1/run/d53e1b2f-3572-40c8-84ab-725106ee858f"
LANGFLOW_API_KEY = os.environ.get("LANGFLOW_API_KEY", "")  # If needed
HF_API_KEY = os.environ.get("HF_API_KEY", "") 

def call_langflow(message, history):
    """
    Call Langflow API and return the response
    """
    headers = {
        "Content-Type": "application/json",
    }
    
    # Add API key if needed
    if LANGFLOW_API_KEY:
       headers["Authorization"] = f"Bearer {LANGFLOW_API_KEY}"
    if HF_API_KEY: 
        headers["x-api-key"] = f"{HF_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 Gradio Chat Interface
demo = gr.ChatInterface(
    fn=call_langflow,
    title="My Langflow Chatbot",
    description="Chat with my AI assistant powered by Langflow",
    examples=["Hello!", "What can you help me with?"],
    theme=gr.themes.Soft(),
    retry_btn=None,
    undo_btn=None,
)

if __name__ == "__main__":
    demo.launch()