Spaces:
Sleeping
Sleeping
File size: 3,564 Bytes
2fb4096 c079a2f 5966d25 cda1e78 2fb4096 cda1e78 5966d25 c079a2f 5966d25 cda1e78 5966d25 2fb4096 5966d25 c079a2f 2fb4096 c079a2f 5966d25 2fb4096 5966d25 2fb4096 c079a2f 2fb4096 c079a2f 2fb4096 c079a2f 2fb4096 c079a2f 2fb4096 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
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()
|