Update app.py

app.py

@@ -1,65 +1,31 @@
-import gradio as gr
-from transformers import AutoModelForImageClassification, AutoImageProcessor
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import torch
 
-# Load multiple models
-model_names = [
-    "dima806/deepfake_vs_real_image_detection",
-    "BuzzFeedNews/Deepfake-Detection",
-    "umarlai/deepfake-detection-vit"
-]
+# ✅ Use a valid Hugging Face model repo
+MODEL_NAME = "selimsef/dfdc_deepfake_challenge"
 
-models = []
-processors = []
+# Load processor & model
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
 
-for name in model_names:
-    processors.append(AutoImageProcessor.from_pretrained(name))
-    models.append(AutoModelForImageClassification.from_pretrained(name))
+# Load your image
+image_path = "your_image.jpg"  # replace with your raw image path
+image = Image.open(image_path).convert("RGB")
 
-def predict(image):
-    votes = {"Real": 0, "Fake": 0}
-    probs_list = []
+# Preprocess
+inputs = processor(images=image, return_tensors="pt")
 
-    for model, processor in zip(models, processors):
-        inputs = processor(images=image, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model(**inputs)
-            probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
-        
-        # Map outputs depending on model’s labels
-        labels = model.config.id2label
-        result = {labels[i]: float(probs[i]) for i in range(len(labels))}
+# Inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probs = torch.nn.functional.softmax(logits, dim=-1)[0]
 
-        # Normalize labels to Real/Fake
-        real_score = result.get("Real", result.get("REAL", result.get("0", 0)))
-        fake_score = result.get("Fake", result.get("FAKE", result.get("1", 0)))
+# Get labels
+labels = model.config.id2label
+real_prob = probs[labels["0"]] if "0" in labels else probs[0]
+fake_prob = probs[labels["1"]] if "1" in labels else probs[1]
 
-        probs_list.append({"Real": real_score, "Fake": fake_score})
+print(f"Prediction: Real={real_prob:.4f}, Fake={fake_prob:.4f}")
 
-        if fake_score > real_score:
-            votes["Fake"] += 1
-        else:
-            votes["Real"] += 1
-
-    # Majority voting
-    final_label = "Fake" if votes["Fake"] > votes["Real"] else "Real"
-
-    # Average probability
-    avg_real = sum([p["Real"] for p in probs_list]) / len(probs_list)
-    avg_fake = sum([p["Fake"] for p in probs_list]) / len(probs_list)
-
-    return {final_label: max(avg_real, avg_fake), "Real": avg_real, "Fake": avg_fake}
-
-# UI
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="label",
-    title="🕵️ Deepfake Detector (Ensemble)",
-    description="Uploads an image and checks if it's REAL or FAKE using 3 different models combined."
-)
-
-if __name__ == "__main__":
-    demo.launch()
-