Update app.py

app.py

@@ -1,83 +1,33 @@
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
-import requests
-from serpapi import GoogleSearch
-import tempfile
-
-# Replace with your actual API keys
-IMGBB_API_KEY = "47cf5c6318ae2fc7d1ea3305189942c4"
-SERPAPI_API_KEY = "d70b432deb7b0acfb440fada51e1446974913bb30cc753e78397752336b33db9"
 
 # Load the deepfake detection model
 deepfake_detector = pipeline("image-classification", model="Wvolf/ViT_Deepfake_Detection")
 
-# Upload to ImgBB
-def upload_image_to_imgbb(image, api_key):
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
-        image.save(temp_file.name)
-        with open(temp_file.name, 'rb') as f:
-            response = requests.post(
-                "https://api.imgbb.com/1/upload",
-                data={'key': api_key},
-                files={'image': f}
-            )
-    if response.status_code == 200:
-        return response.json()['data']['url']
-    else:
-        return None
-
-# Search via SerpAPI
-def search_deepfakes(query, api_key):
-    params = {
-        "q": query,
-        "api_key": api_key,
-        "engine": "google",
-        "search_type": "image"
-    }
-    search = GoogleSearch(params)
-    results = search.get_dict()
-    return results.get("images_results", [])
-
-# Main function
-def detect_and_search(image):
+def detect_deepfake(image):
     result = deepfake_detector(image)
     prediction = max(result, key=lambda x: x['score'])
-    label = prediction['label']
+    label = prediction['label'].lower()
     confidence = prediction['score'] * 100
-    threshold = 50.0
-
-    if prediction['score'] > threshold / 100:
-        status = f"🚨 Deepfake Detected! FAKE with {confidence:.2f}% confidence."
-    else:
-        status = f"✅ REAL image with {confidence:.2f}% confidence."
 
-    # Upload to ImgBB
-    image_url = upload_image_to_imgbb(image, IMGBB_API_KEY)
-    search_summary = ""
-    if image_url:
-        query = f"deepfake {image_url}"
-        results = search_deepfakes(query, SERPAPI_API_KEY)
-        if results:
-            first_result = results[0]
-            search_summary = (
-                f"\n🔎 Found {len(results)} visually similar images online.\n"
-                f"Example:\n- {first_result['title']}\n- {first_result['link']}"
-            )
-        else:
-            search_summary = "\n🔍 No similar deepfakes found online."
+    # Adjusted threshold logic
+    if label == "fake" and confidence > 75:
+        verdict = f"🚨 Deepfake Detected! FAKE with {confidence:.2f}% confidence."
+    elif label == "real" and confidence > 75:
+        verdict = f"✅ REAL image with {confidence:.2f}% confidence."
     else:
-        search_summary = "\n⚠️ Error uploading image for reverse search."
+        verdict = f"⚠️ Uncertain — low confidence: {label.upper()} with {confidence:.2f}% confidence."
 
-    return status + search_summary
+    return verdict
 
-# Gradio Interface
+# Gradio UI — upload only (no webcam)
 iface = gr.Interface(
-    fn=detect_and_search,
-    inputs=gr.Image(type="pil", label="Upload or Capture Image"),
+    fn=detect_deepfake,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
     outputs="text",
-    title="Deepfake Confidence & Reverse Image Search",
-    description="Upload or capture an image. This tool detects deepfakes and checks the internet for visually similar images using SerpAPI."
+    title="Deepfake Confidence Score Detector",
+    description="Upload an image to check if it's a deepfake. Powered by Hugging Face Transformers."
 )
 
 if __name__ == "__main__":