Update app.py

app.py

@@ -1,34 +1,25 @@
 import gradio as gr
 from transformers import pipeline
 
-
-# Load the models for image and video classification
-#image_model = pipeline("image-classification", model="Rohit1412/Deepfake", device=0)
 video_model = pipeline("video-classification", model="Rohit1412/deepfakerohit2.0")
 
-def classify_input(input_data):
-    # Check if the input is an image or a video
-    if isinstance(input_data, str) and input_data.endswith(('.mp4', '.mov', '.avi')):
-        # Classify the uploaded video
-        predictions = video_model(input_data)
-        # Create a dictionary of labels and their corresponding scores
-        result = {pred["label"]: pred["score"] for pred in predictions}
-        return result
-    else:
-        # Classify the uploaded image
-        predictions = image_model(input_data)
-        return predictions[0]['label'], predictions[0]['score']
+def classify_video(video):
+    # 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}
+    
+    # Return the result dictionary
+    return result
 
 # Create Gradio interface
 interface = gr.Interface(
-    fn=classify_input,
-    inputs=gr.inputs.Audio(source="upload", type="filepath", label="Upload Image or Video"),
-    outputs=[
-        gr.Label(num_top_classes=3, label="Predictions"),
-        gr.Textbox(label="Confidence", visible=False)  # Hide confidence for video output
-    ],
-    title="Image and Video Classification App",
-    description="Upload an image or a video to classify its content. Note: The model is still under training; it may give incorrect outputs."
+    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