Update app.py

app.py

@@ -1,44 +1,29 @@
 from transformers import pipeline
 import gradio as gr
 
-# Load the models
-image_model = pipeline("image-classification", model="Rohit1412/Deepfake", device=0)
-video_model = pipeline("video-classification", model="Rohit1412/deepfakerohit2.0")
+from transformers import pipeline
 
-def classify_image(image):
-    try:
-        predictions = image_model(image)
-        result = {pred["label"]: pred["score"] for pred in predictions}
-        return result
-    except Exception as e:
-        return {"error": f"An error occurred during image classification Make sure you choose the correct model: {str(e)}"}
+video_model = pipeline("video-classification", model="Rohit1412/deepfakerohit2.0")
 
 def classify_video(video):
-    try:
-        predictions = video_model(video)
-        result = {pred["label"]: pred["score"] for pred in predictions}
-        return result
-    except Exception as e:
-        return {"error": f"An error occurred during video classification Make sure you choose the correct model: {str(e)}"}
-
-# Create Gradio Blocks interface
-with gr.Blocks() as app:
-    gr.Markdown("# Video and Image Classification App")
+    # Classify the uploaded video and return the results
+    predictions = video_model(video)
+    
+    # Create a dictionary of labels and their corresponding scores
+    result = {pred["label"]: pred["score"] for pred in predictions}
     
-    with gr.Tab("Image Classification"):
-        image_input = gr.Image(label="Upload Image")
-        image_output = gr.Label(num_top_classes=3, label="Predictions")
-        image_button = gr.Button("Classify Image")
-        
-        image_button.click(fn=classify_image, inputs=image_input, outputs=image_output)
+    # Return the result dictionary
+    return result
 
-    with gr.Tab("Video Classification"):
-        video_input = gr.Video(label="Upload Video")
-        video_output = gr.Label(num_top_classes=3, label="Predictions")
-        video_button = gr.Button("Classify Video")
-        
-        video_button.click(fn=classify_video, inputs=video_input, outputs=video_output)
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+    title="Video Classification App",
+    description="Upload a video to classify its content."
+)
 
 # Launch the interface
 if __name__ == "__main__":
-    app.launch(debug=True)
+    interface.launch(debug=True)