Update app.py

app.py

@@ -4,6 +4,7 @@ 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()
@@ -12,8 +13,6 @@ OWNER_PHONE_NUMBER = os.getenv("OWNER_PHONE_NUMBER")
 
 # --- AI Model Setup ---
 print("Loading AI Image Detection model...")
-# FIX 1: Use the correct pipeline for this task.
-# 'zero-shot-image-classification' is designed to work with 'candidate_labels'.
 image_detector: Pipeline = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
 print("✅ Model loaded successfully.")
 
@@ -33,10 +32,16 @@ def analyze_image_authenticity(image_url: str) -> dict:
     
     print(f"Analyzing image from URL: {image_url}")
     try:
-        # FIX 2: Add a User-Agent header to mimic a real browser request.
-        # This prevents websites (including Puch's image hosting) from blocking the download.
+        # --- 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'}
-        image = Image.open(requests.get(image_url, stream=True, timeout=10, headers=headers).raw)
+        
+        # 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)}")