Update app.py

app.py

@@ -67,7 +67,6 @@ backend = import_backend_script("app.py")  # Import app.py from the cloned repos
 # Initialize the analyzer instance from the imported module
 analyzer = backend.DeepfakeAnalyzer()  # Use the imported module's class or function
 
-# Define the Gradio function to analyze the video
 # Define the Gradio function to analyze the video
 def analyze_video(video_file):
     # Truncate the video to 15 seconds
@@ -75,28 +74,33 @@ def analyze_video(video_file):
     
     # Pass the truncated video to the analyzer
     results = analyzer.analyze_media(truncated_video)
+    
+    # Get the combined assessment probability
     combined_probability = results['combined_assessment']
-    audio_analysis = results["audio_analysis"]
-    video_probability = results['video_analysis']['probability']
-    frame_count = len(results['video_analysis']['frame_results'])
-
-    # Format the output for display
-    output = {
-        "Audio Analysis": audio_analysis,
-        "Deepfake Probability (Combined Assessment)": f"{combined_probability:.2f}%",
-        "Video Analysis": {
-            "Deepfake Probability": f"{video_probability:.4f}",
-            "Frames Analyzed": frame_count,
-            "Frame Analysis Summary": [
-                {
-                    "Probability": frame_result["probability"]
-                }
-                for frame_result in results['video_analysis']['frame_results']
-            ]
-        }
-    }
-    return output
+    
+    # Interpret the result as genuine or deepfake based on probability threshold
+    if combined_probability < 50:
+        analysis_result = "genuine/original"
+    else:
+        analysis_result = "a deepfake"
+    
+    # Create a readable output message
+    output_text = (
+        f"According to our analysis, the video you uploaded appears to be {analysis_result} "
+        f"with a {combined_probability:.2f}% probability. "
+        f"{len(results['video_analysis']['frame_results'])} frames were analyzed in total."
+    )
+    
+    return output_text
 
+# Define the Gradio interface with a text output for readability
+interface = gr.Interface(
+    fn=analyze_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs="text",
+    title="AllMark - Deepfake Analyzer",
+    description="Upload a video to analyze for deepfake content. Get an assessment of the likelihood that the video is genuine or a deepfake."
+)
 
 # Define the Gradio interface
 interface = gr.Interface(