app.py

import gradio as gr
import os
import tempfile
from Fight_detec_func import fight_detec
from objec_detect_yolo import detection
import time # Added for unique temp file names

def analyze_video(video_file_obj):
    if video_file_obj 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}")

    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)

        # 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.

        results = {
            "Fight Detection": fight_status,
            "Detected Objects": detected_objects_list
        }

    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}")


    return results

# Interface Definition
iface = gr.Interface(
    fn=analyze_video,
    inputs=gr.Video(label="Upload Video"), # Source can be 'upload' or 'webcam'
    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()