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1daceba 1ee7d81 1daceba 3a89a2a 1daceba e8f198f 1daceba 1ee7d81 0c6bb41 1ee7d81 1daceba e8f198f 1daceba 0c6bb41 e8f198f 1daceba e8f198f 0c6bb41 e8f198f 0c6bb41 e8f198f 1daceba abc3d93 | 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
from PIL import Image
from transformers import pipeline, Pipeline
import os
from dotenv import load_dotenv
import io # <-- NEW: Import the 'io' library
# --- Configuration ---
load_dotenv()
VALID_BEARER_TOKEN = os.getenv("VALID_BEARER_TOKEN")
OWNER_PHONE_NUMBER = os.getenv("OWNER_PHONE_NUMBER")
# --- AI Model Setup ---
print("Loading AI Image Detection model...")
image_detector: Pipeline = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32", local_files_only=True)
print("✅ Model loaded successfully.")
# --- Tool 1: The Main Analysis Function ---
def analyze_image_authenticity(image_url: str) -> dict:
"""
Analyzes an image from a URL to determine if it is real or AI-generated.
Args:
image_url: The URL of the image to analyze.
Returns:
A dictionary with the analysis results and probability scores.
"""
if not image_url:
raise gr.Error("Image URL parameter is missing.")
print(f"Analyzing image from URL: {image_url}")
try:
# --- MODIFIED IMAGE DOWNLOAD BLOCK ---
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'}
# 1. Let requests handle the full download and any redirects
response = requests.get(image_url, timeout=15, headers=headers)
response.raise_for_status() # Raise an exception for bad status codes (like 404 or 500)
# 2. Open the image from the downloaded content in memory
image = Image.open(io.BytesIO(response.content))
# --- END OF MODIFIED BLOCK ---
except Exception as e:
raise gr.Error(f"Could not load image from URL. Error: {str(e)}")
labels = ["a real photograph", "a computer-generated image", "an illustration or drawing"]
results = image_detector(image, candidate_labels=labels)
print(f"Analysis successful. Results: {results}")
return {"analysis_results": results}
# --- Tool 2: The Validation Function for Sharing ---
def validate() -> str:
"""
Validates the server ownership for sharing. Returns the owner's phone number.
Returns:
The owner's phone number as a string.
"""
return OWNER_PHONE_NUMBER
# --- Gradio Interface ---
demo = gr.TabbedInterface(
[
gr.Interface(
fn=analyze_image_authenticity,
inputs=[gr.Textbox(label="Image URL")],
outputs=[gr.JSON(label="Analysis Results")],
title="AI Image Authenticity Detector"
),
gr.Interface(
fn=validate,
inputs=[],
outputs="text",
title="Validation Tool"
)
],
["Image Analyzer", "Validator"]
)
# --- Launch the App and MCP Server ---
demo.launch(mcp_server=True) |