Create app.py for the KYC verification

app.py

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+import cv2
+import gradio as gr
+from PIL import Image
+from transformers import pipeline
+
+# Step 2: Load the AI Hunter (The Model)
+# This single line downloads the "Deepfake Hunter" model
+# It is trained to recognize 'Fake' (Class 0) vs 'Real' (Class 1)
+v_detector = pipeline("image-classification", model="prithivMLmods/Deepfake-Detect-Siglip2")
+
+# Step 3: The "Video Slicer" (OpenCV)
+def analyze_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frame_scores = []
+    
+    # Slice the video: Grab 1 frame every 30 frames (roughly 1 frame per second)
+    count = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret: break
+        
+        if count % 30 == 0:
+            # Convert the video frame to a picture the AI can read
+            color_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            pil_img = Image.fromarray(color_frame)
+            
+            # Ask the AI: "Is this real?"
+            prediction = v_detector(pil_img)
+            # Store the 'Real' confidence score
+            real_score = [res['score'] for res in prediction if res['label'] == 'Real']
+            if real_score:
+                frame_scores.append(real_score[0])
+            
+        count += 1
+    cap.release()
+    
+    if not frame_scores:
+        return "ERROR: Could not read video frames"
+        
+    # Step 4: Give the final verdict
+    avg_score = sum(frame_scores) / len(frame_scores)
+    return "REAL" if avg_score > 0.5 else "DEEPFAKE"
+
+# Step 4: The IOB Dashboard (Gradio Demo)
+with gr.Blocks(title="IOB Sentinel") as demo:
+    gr.Markdown("# IOB Sentinel: Video KYC Deepfake Detector")
+    gr.Markdown("Analyzing visual artifacts, skin texture, and lighting inconsistencies.")
+    
+    with gr.Tabs():
+        with gr.TabItem("Upload Video"):
+            upload_input = gr.Video(label="Upload KYC Video")
+            upload_output = gr.Textbox(label="Result", text_align="center", show_copy_button=True)
+            upload_btn = gr.Button("Analyze Uploaded Video", variant="primary")
+            upload_btn.click(fn=analyze_video, inputs=upload_input, outputs=upload_output)
+            
+        with gr.TabItem("Live Webcam"):
+            # Gradio lets you record via webcam using sources=["webcam"]
+            webcam_input = gr.Video(sources=["webcam"], label="Record Live Video")
+            webcam_output = gr.Textbox(label="Result", text_align="center", show_copy_button=True)
+            webcam_btn = gr.Button("Analyze Live Video", variant="primary")
+            webcam_btn.click(fn=analyze_video, inputs=webcam_input, outputs=webcam_output)
+
+if __name__ == "__main__":
+    demo.launch()