Update app.py
Browse files
app.py
CHANGED
@@ -1,39 +1,100 @@
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import gradio as gr
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import os
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import tempfile
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from Fight_detec_func import fight_detec
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from objec_detect_yolo import
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iface = gr.Interface(
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fn=analyze_video,
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inputs=gr.Video(label="Upload Video"),
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outputs=gr.JSON(label="Detection Results"),
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title="Fight and Object Detection
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description="Upload a video to detect fights and objects
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)
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--- START OF FILE app.py ---
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import gradio as gr
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import os
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import tempfile
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from Fight_detec_func import fight_detec
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from objec_detect_yolo import detection
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import time # Added for unique temp file names
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def analyze_video(video_file_obj):
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if video_file_obj is None:
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return {"Error": "No video file uploaded."}
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temp_dir = tempfile.mkdtemp()
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# Create a unique filename within the temp dir
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input_filename = os.path.basename(video_file_obj.name)
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base, ext = os.path.splitext(input_filename)
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unique_suffix = str(int(time.time() * 1000)) # Add timestamp for uniqueness
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safe_base = "".join(c if c.isalnum() or c in ('-', '_') else '_' for c in base) # Sanitize name
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video_path = os.path.join(temp_dir, f"{safe_base}_{unique_suffix}{ext}")
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try:
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# Gradio file object has '.name' attribute with the path to temp copy
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# Copy it to ensure control over the path and name if needed downstream
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with open(video_path, 'wb') as f_dst, open(video_file_obj.name, 'rb') as f_src:
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f_dst.write(f_src.read())
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print(f"Processing video: {video_path}")
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# Run detection functions
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# fight_detec returns (result_string, prediction_score)
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# detection returns (set_of_labels, output_video_path)
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fight_status, _ = fight_detec(video_path, debug=False)
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detected_objects_set, annotated_video_path = detection(video_path)
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# Format results
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# Convert set to sorted list for consistent JSON output
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detected_objects_list = sorted(list(detected_objects_set))
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print(f"Fight Status: {fight_status}")
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print(f"Detected Objects: {detected_objects_list}")
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# Note: annotated_video_path points to a file saved in the 'results' directory
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# within the Space container, but we are not returning it via the UI here.
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results = {
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"Fight Detection": fight_status,
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"Detected Objects": detected_objects_list
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}
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except Exception as e:
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print(f"Error during processing: {e}")
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results = {"Error": f"Processing failed: {str(e)}"}
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finally:
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# Clean up the specific temp file and directory
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if 'video_path' in locals() and os.path.exists(video_path):
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try:
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os.remove(video_path)
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print(f"Removed temp video file: {video_path}")
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except OSError as e:
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print(f"Error removing temp file {video_path}: {e}")
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if 'temp_dir' in locals() and os.path.exists(temp_dir):
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try:
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# Clean up the results dir created by objec_detect_yolo if it's inside temp_dir
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results_dir_path = os.path.join(temp_dir, "results")
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if os.path.exists(results_dir_path) and os.path.isdir(results_dir_path):
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# Remove files inside results dir first
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for item in os.listdir(results_dir_path):
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item_path = os.path.join(results_dir_path, item)
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if os.path.isfile(item_path):
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os.remove(item_path)
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os.rmdir(results_dir_path) # Now remove empty results dir
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print(f"Removed temp results directory: {results_dir_path}")
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os.rmdir(temp_dir) # Attempt to remove the main temp dir
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print(f"Removed temp directory: {temp_dir}")
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except OSError as e:
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# Might fail if other files are present or dir not empty
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print(f"Error removing temp directory {temp_dir} or its contents: {e}")
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return results
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# Interface Definition
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iface = gr.Interface(
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fn=analyze_video,
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inputs=gr.Video(label="Upload Video"), # Source can be 'upload' or 'webcam'
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outputs=gr.JSON(label="Detection Results"),
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title="Fight and Object Detection Analysis",
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description="Upload a video (< 1 min recommended) to detect potential fights and specific objects (Fire, Gun, Knife, Smoke, License_Plate). Results appear as JSON.",
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allow_flagging='never',
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examples=[
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# Add paths to example videos if you upload them to the HF repo
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# e.g., ["example_fight.mp4"], ["example_normal_gun.mp4"]
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]
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)
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# Launch the interface
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if __name__ == "__main__":
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iface.launch()
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--- END OF FILE app.py ---
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