app.py

import gradio as gr
import os
# 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 traceback # For better error logging

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."}

    # video_filepath *is* the path to the temporary file created by Gradio
    print(f"Processing video: {video_filepath}")

    try:
        # 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
        detected_objects_list = sorted(list(detected_objects_set))

        print(f"Fight Status: {fight_status}")
        print(f"Detected Objects: {detected_objects_list}")
        # 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,
            "Detected Objects": detected_objects_list
        }

    except Exception as 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 (remains the same)
iface = gr.Interface(
    fn=analyze_video,
    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.",
    allow_flagging='never',
    examples=[
        # Add paths to example videos if you upload them to the HF repo
        # e.g., ["example_fight.mp4"], ["example_normal_gun.mp4"]
    ]
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()