Update app.py

app.py

@@ -2,45 +2,32 @@
 
 import gradio as gr
 import os
-import tempfile
+# No longer need tempfile or time here, Gradio manages the input temp file
 from Fight_detec_func import fight_detec
 from objec_detect_yolo import detection
-import time # Added for unique temp file names
+import traceback # For better error logging
 
-def analyze_video(video_file_obj):
-    if video_file_obj is None:
+def analyze_video(video_filepath): # RENAMED parameter to reflect it's a string path
+    if video_filepath is None:
         return {"Error": "No video file uploaded."}
 
-    temp_dir = tempfile.mkdtemp()
-    # Create a unique filename within the temp dir
-    input_filename = os.path.basename(video_file_obj.name)
-    base, ext = os.path.splitext(input_filename)
-    unique_suffix = str(int(time.time() * 1000)) # Add timestamp for uniqueness
-    safe_base = "".join(c if c.isalnum() or c in ('-', '_') else '_' for c in base) # Sanitize name
-    video_path = os.path.join(temp_dir, f"{safe_base}_{unique_suffix}{ext}")
+    # video_filepath *is* the path to the temporary file created by Gradio
+    print(f"Processing video: {video_filepath}")
 
     try:
-        # Gradio file object has '.name' attribute with the path to temp copy
-        # Copy it to ensure control over the path and name if needed downstream
-        with open(video_path, 'wb') as f_dst, open(video_file_obj.name, 'rb') as f_src:
-             f_dst.write(f_src.read())
-
-        print(f"Processing video: {video_path}")
-
-        # Run detection functions
-        # fight_detec returns (result_string, prediction_score)
-        # detection returns (set_of_labels, output_video_path)
-        fight_status, _ = fight_detec(video_path, debug=False)
-        detected_objects_set, annotated_video_path = detection(video_path)
+        # Directly use the filepath provided by Gradio for analysis
+        # No need to copy the file again.
+        fight_status, _ = fight_detec(video_filepath, debug=False)
+        detected_objects_set, annotated_video_path = detection(video_filepath) # This function saves its own output
 
         # Format results
-        # Convert set to sorted list for consistent JSON output
         detected_objects_list = sorted(list(detected_objects_set))
 
         print(f"Fight Status: {fight_status}")
         print(f"Detected Objects: {detected_objects_list}")
-        # Note: annotated_video_path points to a file saved in the 'results' directory
-        # within the Space container, but we are not returning it via the UI here.
+        # annotated_video_path points to the video saved by detection(),
+        # but we are not returning it to the user via JSON here.
+        # It exists within the Space's filesystem in the 'results' folder.
 
         results = {
             "Fight Detection": fight_status,
@@ -48,42 +35,23 @@ def analyze_video(video_file_obj):
         }
 
     except Exception as e:
-        print(f"Error during processing: {e}")
-        results = {"Error": f"Processing failed: {str(e)}"}
-    finally:
-        # Clean up the specific temp file and directory
-        if 'video_path' in locals() and os.path.exists(video_path):
-            try:
-                os.remove(video_path)
-                print(f"Removed temp video file: {video_path}")
-            except OSError as e:
-                print(f"Error removing temp file {video_path}: {e}")
-        if 'temp_dir' in locals() and os.path.exists(temp_dir):
-            try:
-                # Clean up the results dir created by objec_detect_yolo if it's inside temp_dir
-                results_dir_path = os.path.join(temp_dir, "results")
-                if os.path.exists(results_dir_path) and os.path.isdir(results_dir_path):
-                    # Remove files inside results dir first
-                    for item in os.listdir(results_dir_path):
-                        item_path = os.path.join(results_dir_path, item)
-                        if os.path.isfile(item_path):
-                            os.remove(item_path)
-                    os.rmdir(results_dir_path) # Now remove empty results dir
-                    print(f"Removed temp results directory: {results_dir_path}")
-
-                os.rmdir(temp_dir) # Attempt to remove the main temp dir
-                print(f"Removed temp directory: {temp_dir}")
-            except OSError as e:
-                # Might fail if other files are present or dir not empty
-                print(f"Error removing temp directory {temp_dir} or its contents: {e}")
+        print(f"Error during processing video: {video_filepath}")
+        print(f"Error type: {type(e).__name__}")
+        print(f"Error message: {e}")
+        print("Traceback:")
+        traceback.print_exc() # Print detailed traceback to Space logs
+        results = {"Error": f"Processing failed. Check Space logs for details. Error: {str(e)}"}
 
+    # No explicit cleanup needed for video_filepath, Gradio handles its temporary input file.
+    # Cleanup for files created by detection() (like annotated_video_path)
+    # would ideally happen within that function or rely on the Space's ephemeral nature.
 
     return results
 
-# Interface Definition
+# Interface Definition (remains the same)
 iface = gr.Interface(
     fn=analyze_video,
-    inputs=gr.Video(label="Upload Video"), # Source can be 'upload' or 'webcam'
+    inputs=gr.Video(label="Upload Video"),
     outputs=gr.JSON(label="Detection Results"),
     title="Fight and Object Detection Analysis",
     description="Upload a video (< 1 min recommended) to detect potential fights and specific objects (Fire, Gun, Knife, Smoke, License_Plate). Results appear as JSON.",
@@ -97,3 +65,4 @@ iface = gr.Interface(
 # Launch the interface
 if __name__ == "__main__":
     iface.launch()
+