Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+import cv2
+import os
+import uuid
+
+# Load model
+model_name = "prithivMLmods/deepfake-detector-model-v1"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = SiglipForImageClassification.from_pretrained(model_name)
+
+def analyze_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    result_labels = []
+    frame_skip = 10
+    count = 0
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if count % frame_skip == 0:
+            try:
+                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                pil = Image.fromarray(rgb)
+                inputs = processor(images=pil, return_tensors="pt")
+                with torch.no_grad():
+                    logits = model(**inputs).logits
+                    pred = torch.argmax(logits, dim=1).item()
+                    label = model.config.id2label[pred]
+                    result_labels.append(label)
+            except:
+                continue
+        count += 1
+
+    cap.release()
+
+    real = result_labels.count("REAL")
+    fake = result_labels.count("FAKE")
+    final = "REAL" if real > fake else "FAKE"
+
+    return f"🟢 REAL frames: {real} | 🔴 FAKE frames: {fake} → Final verdict: **{final}**"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=analyze_video,
+    inputs=gr.Video(label="Upload a video"),
+    outputs=gr.Markdown(label="Result"),
+    title="🎭 Deepfake Video Detector",
+    description="Upload a video (MP4). The model will analyze it and return whether it's REAL or FAKE based on detected face frames."
+)
+
+demo.launch()