Update app.py

app.py

@@ -68,6 +68,8 @@ backend = import_backend_script("app.py")  # Import app.py from the cloned repos
 analyzer = backend.DeepfakeAnalyzer()  # Use the imported module's class or function
 
 # Define the Gradio function to analyze the video
+import json  # Ensure imports include json for dictionary-to-JSON string handling
+
 def analyze_video(video_file):
     try:
         # Truncate the video to 15 seconds
@@ -76,41 +78,31 @@ def analyze_video(video_file):
         # Pass the truncated video to the analyzer
         results = analyzer.analyze_media(truncated_video)
         
-        # Check combined probability to interpret the result
+        # Get the combined probability and interpret the result
         combined_probability = results.get('combined_assessment', 0)
         analysis_result = "genuine/original" if combined_probability < 50 else "a deepfake"
         
-        # Construct the 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."
-        )
+        # Prepare JSON output
+        output = {
+            "message": 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
+        return output
 
     except Exception as e:
-        # Log the error and return a user-friendly message
+        # Log the error and return a JSON-compatible error message
         logging.error(f"Error during analysis: {e}")
-        return "An error occurred during video analysis. Please check your input and try again."
-
-
-# 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."
-)
+        return {"error": "An error occurred during video analysis. Please check your input and try again."}
 
-# Define the Gradio interface
+# Define the Gradio interface with json output to handle dictionaries
 interface = gr.Interface(
     fn=analyze_video,
     inputs=gr.Video(label="Upload Video"),
     outputs="json",
     title="AllMark - Deepfake Analyzer",
-    description="Upload a video to analyze for deepfake content. N.B. - Average processing time is between 1 to 5 minutes."
+    description="Upload a video to analyze for deepfake content. Get an assessment of the likelihood that the video is genuine or a deepfake."
 )
 
 # Launch Gradio app