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| 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() | |