app.py

import gradio as gr
import requests
import os

# Load API URL and token from environment variables
API_URL = os.getenv("HF_API_URL", "https://api-inference.huggingface.co/models/rahul7star/fastai-rahul-text-model-v02")
API_TOKEN = os.getenv("HF_API_TOKEN", "your-default-token")  # Replace with your actual token for fallback

# Function to call the Hugging Face Inference API
def call_huggingface_api(input_text):
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    payload = {"inputs": input_text}
    try:
        print(f"Request sent to: {API_URL}")
        print(f"Payload: {payload}")

        # Make the API call
        response = requests.post(API_URL, headers=headers, json=payload)
        
        print(f"Response Status Code: {response.status_code}")

        if response.status_code == 200:
            data = response.json()
            print(f"Response Data: {data}")
            # Assuming the model returns 'answer' and 'confidence'
            return f"Question: {input_text}\nAnswer: {data.get('answer', 'No answer found.')}\nConfidence: {data.get('confidence', 'N/A')}"
        else:
            print(f"Error Response: {response.text}")
            return f"Error: {response.status_code} - {response.text}"

    except requests.exceptions.RequestException as e:
        error_message = f"Network error during API call: {e}"
        print(error_message)
        return error_message

    except ValueError as e:
        error_message = f"Error parsing response JSON: {e}"
        print(error_message)
        return error_message

    except KeyError as e:
        error_message = f"KeyError: Missing expected key in response JSON: {e}"
        print(error_message)
        return error_message

    except Exception as e:
        error_message = f"Unexpected error during API call: {e}"
        print(error_message)
        return error_message


# Example of how you could set up specific queries about you (Rahul7star)
def ask_about_rahul7star(input_text):
    # Example questions about your career, hobbies, and interests
    predefined_answers = {
        "Who is rahul7star?": "Rahul7star is a software developer and AI creator based in NSW. He is passionate about coding and AI.",
        "What does Rahul7star do?": "Rahul7star works as a developer and enjoys solving complex coding problems. He loves traveling and exploring new destinations.",
        "Tell me about Rahul7star's hobbies?": "Rahul7star enjoys driving scenic routes, having a cold beer after work, and traveling to new destinations, especially to places like Iceland to witness the Northern Lights.",
        "What is Rahul7star known for?": "He is known for his work in AI, software development, and his ability to solve complex coding challenges."
    }

    # Check if the input matches any predefined question
    if input_text in predefined_answers:
        return predefined_answers[input_text]
    else:
        # If the question isn't predefined, call the Hugging Face model API
        return call_huggingface_api(input_text)


# Gradio Interface for the AI agent
gr.Interface(
    fn=ask_about_rahul7star,
    inputs="text",
    outputs="text",
    examples=[
        ["Who is rahul7star?"],
        ["What does Rahul7star do?"],
        ["Tell me about Rahul7star's hobbies?"],
        ["What is Rahul7star known for?"]
    ],
    title="Ask Rahul7star AI",
    description="Ask questions about Rahul7star and get personalized answers powered by Hugging Face Inference API. Feel free to ask about his career, hobbies, or anything else."
).launch()