Upload 4 files

app.py

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+import gradio as gr
+import torch
+import mimetypes
+from PIL import Image
+import cv2
+from torchvision.models import efficientnet_b0
+from torchvision import transforms
+
+# =========================
+# Load Model
+# =========================
+def load_model():
+    model = efficientnet_b0()
+    model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, 2)
+    model.load_state_dict(torch.load("models/best_model-v3.pt", map_location="cpu"))
+    model.eval()
+    return model
+
+model = load_model()
+
+# =========================
+# Preprocessing
+# =========================
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+# =========================
+# Image Prediction
+# =========================
+def predict_image(path):
+    img = Image.open(path).convert("RGB")
+    tensor = preprocess(img).unsqueeze(0)
+
+    with torch.no_grad():
+        out = model(tensor)
+        probs = torch.softmax(out, dim=1)[0]
+        conf, pred = torch.max(probs, dim=0)
+
+    label = "🟢 Real" if pred.item() == 0 else "🔴 Deepfake"
+    return label, f"{conf.item()*100:.2f}%", img
+
+
+# =========================
+# Video Prediction (Every 10th Frame)
+# =========================
+def predict_video(path):
+    cap = cv2.VideoCapture(path)
+
+    frame_count = 0
+    predictions = []
+    preview_img = None
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Process every 10th frame
+        if frame_count % 10 == 0:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            img = Image.fromarray(frame_rgb)
+
+            # Save first sampled frame for preview
+            if preview_img is None:
+                preview_img = img
+
+            tensor = preprocess(img).unsqueeze(0)
+
+            with torch.no_grad():
+                out = model(tensor)
+                probs = torch.softmax(out, dim=1)[0]
+                predictions.append(probs)
+
+        frame_count += 1
+
+    cap.release()
+
+    if len(predictions) == 0:
+        return "❌ No valid frames found", "", None
+
+    # Average all frame probabilities
+    avg_probs = torch.stack(predictions).mean(dim=0)
+    conf, pred = torch.max(avg_probs, dim=0)
+
+    label = "🟢 Real (Multi-frame)" if pred.item() == 0 else "🔴 Deepfake (Multi-frame)"
+
+    return label, f"{conf.item()*100:.2f}%", preview_img
+
+
+# =========================
+# Main Prediction Router
+# =========================
+def predict_file(file_obj):
+    if file_obj is None:
+        return "⚠️ No file selected", "", None
+
+    path = file_obj.name
+    mime, _ = mimetypes.guess_type(path)
+
+    if mime and mime.startswith("image"):
+        return predict_image(path)
+
+    elif mime and mime.startswith("video"):
+        return predict_video(path)
+
+    else:
+        return "Unsupported file type", "", None
+
+
+# =========================
+# Gradio UI
+# =========================
+with gr.Blocks(title="Deepfake Detector") as demo:
+    gr.Markdown("## 🧠 Deepfake Detector\nUpload an image or video to analyze authenticity.")
+
+    file_input = gr.File(
+        label="Drop File Here",
+        file_types=[".jpg", ".jpeg", ".png", ".mp4", ".mov"]
+    )
+
+    with gr.Row():
+        prediction = gr.Textbox(label="Prediction", interactive=False)
+        confidence = gr.Textbox(label="Confidence (%)", interactive=False)
+
+    preview = gr.Image(label="Preview", interactive=False)
+
+    file_input.change(
+        fn=predict_file,
+        inputs=file_input,
+        outputs=[prediction, confidence, preview]
+    )
+
+demo.launch()

deploy_to_hf.py

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+import os
+from huggingface_hub import HfApi, login
+
+# Replace with your Hugging Face Token (e.g., hf_...)
+HF_TOKEN = "your_hugging_face_token_here" 
+
+# Replace with your Hugging Face username and your desired Space name
+REPO_ID = "YourUsername/Deepfake-Image-Detector" 
+
+def deploy():
+    # Login to Hugging Face
+    login(token=HF_TOKEN, add_to_git_credential=True)
+    
+    api = HfApi()
+    print(f"Uploading files to Hugging Face Space: {REPO_ID}...")
+    
+    # Upload the entire current folder to the Space
+    api.upload_folder(
+        folder_path=".",
+        repo_id=REPO_ID,
+        repo_type="space"
+    )
+    
+    print("✅ Successfully deployed! Your space should be building now.")
+
+if __name__ == "__main__":
+    deploy()

models/best_model-v3.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb694a883cf9512a0aa7d28d218485100a217c514fbd4252f72d7a4a8ab98475
+size 16341059

requirements.txt

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+torch
+torchvision
+gradio
+opencv-python-headless
+pillow