initial commit

app.py

@@ -0,0 +1,119 @@
+import os
+import cv2
+import torch
+import numpy as np
+import uuid
+import threading
+import gradio as gr
+from fastapi import FastAPI, UploadFile, File, BackgroundTasks, HTTPException
+from fastapi.responses import FileResponse
+from collections import deque
+from pytorchvideo.models.hub import slowfast_r50
+from ultralytics import YOLO
+import torch.nn as nn
+
+# --- SETUP & DIRECTORIES ---
+UPLOAD_DIR = "uploads"
+OUTPUT_DIR = "outputs"
+MODEL_PATH = "models/best_slowfast_theft.pth"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+JOBS = {} # Track progress
+
+# --- MODEL LOADING ---
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+yolo = YOLO("yolov8n.pt")
+
+def load_slowfast():
+    model = slowfast_r50(pretrained=False)
+    in_features = model.blocks[-1].proj.in_features
+    model.blocks[-1].proj = nn.Sequential(
+        nn.Dropout(p=0.5),
+        nn.Linear(in_features, 2)
+    )
+    if os.path.exists(MODEL_PATH):
+        ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
+        model.load_state_dict(ckpt["model"] if "model" in ckpt else ckpt)
+    model.to(DEVICE).eval()
+    return model
+
+detector_model = load_slowfast()
+
+# --- DETECTION LOGIC ---
+def process_video_logic(job_id, input_path, output_path):
+    cap = cv2.VideoCapture(input_path)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    w, h = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+    frame_buffer = deque(maxlen=32)
+    
+    curr = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret: break
+        
+        curr += 1
+        JOBS[job_id]["progress"] = int((curr/total_frames)*100)
+
+        # Basic YOLO logic (Simplified for speed)
+        results = yolo(frame, verbose=False)
+        for r in results:
+            for box in r.boxes:
+                if int(box.cls[0]) == 0:
+                    x1, y1, x2, y2 = map(int, box.xyxy[0])
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        
+        out.write(frame)
+    
+    cap.release()
+    out.release()
+    JOBS[job_id]["status"] = "completed"
+
+# --- FASTAPI ENDPOINTS ---
+app = FastAPI()
+
+@app.post("/api/detect")
+async def api_detect(background_tasks: BackgroundTasks, file: UploadFile = File(...)):
+    job_id = str(uuid.uuid4())
+    in_p = os.path.join(UPLOAD_DIR, f"{job_id}.mp4")
+    out_p = os.path.join(OUTPUT_DIR, f"{job_id}.mp4")
+    
+    with open(in_p, "wb") as f: f.write(await file.read())
+    
+    JOBS[job_id] = {"progress": 0, "status": "processing", "file": out_p}
+    background_tasks.add_task(process_video_logic, job_id, in_p, out_p)
+    return {"job_id": job_id}
+
+@app.get("/api/progress/{job_id}")
+async def api_progress(job_id: str):
+    return JOBS.get(job_id, {"error": "not found"})
+
+# --- GRADIO FRONTEND ---
+def web_ui_process(video_input):
+    if video_input is None: return None
+    job_id = str(uuid.uuid4())
+    out_p = os.path.join(OUTPUT_DIR, f"{job_id}.mp4")
+    
+    # Run the processing (Sync for Gradio UI to show progress)
+    JOBS[job_id] = {"progress": 0, "status": "processing"}
+    process_video_logic(job_id, video_input, out_p)
+    return out_p
+
+with gr.Blocks(title="Theft Detection System") as demo:
+    gr.Markdown("# 🛡️ AI Theft Detection System")
+    with gr.Row():
+        video_in = gr.Video(label="Upload Video")
+        video_out = gr.Video(label="Processed Result")
+    btn = gr.Button("Detect Theft")
+    btn.click(web_ui_process, inputs=video_in, outputs=video_out)
+
+# --- MOUNT FASTAPI TO GRADIO ---
+# This allows both to run on the same port on Hugging Face
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

models/best_slowfast_theft.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:267ce0e679450fb20400d7a03318c7b9e7d37a0356e396eac0051ba8fb0e2194
+size 135113790

packages.txt

@@ -0,0 +1,3 @@
+libgl1
+libglib2.0-0
+ffmpeg

requirements.txt

@@ -0,0 +1,11 @@
+fastapi
+uvicorn
+gradio
+ultralytics
+torch
+torchvision
+pytorchvideo
+opencv-python
+fvcore
+numpy<2.0.0
+python-multipart