ban jaa bro

Docker

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+# Dockerfile
+# Use a lightweight official Python image
+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /code
+
+# Copy the requirements file and install all dependencies
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy all your application files (backend_app.py, best.pt) into the container
+COPY . /code/
+
+# The command that tells the server how to run your FastAPI app
+CMD ["uvicorn", "backend_app:app", "--host", "0.0.0.0", "--port", "7860"]

backend_app.py

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+import os
+import json
+import tempfile
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import FileResponse
+from ultralytics import YOLO
+import supervision as sv
+
+# --- MODEL AND APP INITIALIZATION ---
+# The model is loaded only ONCE when the server starts, making it fast.
+MODEL_PATH = "best.pt"
+model = YOLO(MODEL_PATH)
+
+app = FastAPI(title="YOLOv8 Tracking API")
+
+# --- YOUR EXISTING PROCESSING LOGIC (UNCHANGED) ---
+# We just put your code inside this function.
+def process_video_logic(input_path, output_path, json_path):
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoxAnnotator(thickness=5)
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.TOP_CENTER, text_scale=1, text_thickness=1)
+    frame_generator = sv.get_video_frames_generator(source_path=input_path)
+    video_info = sv.VideoInfo.from_video_path(input_path)
+    results_list = []
+
+    with sv.VideoSink(target_path=output_path, video_info=video_info) as sink:
+        for frame_number, frame in enumerate(frame_generator):
+            results = model(frame, verbose=False)[0]
+            detections = sv.Detections.from_ultralytics(results)
+            tracked_detections = tracker.update_with_detections(detections=detections)
+            labels = [f"ID: {det[4]} {model.model.names[int(det[3])]}" for det in tracked_detections]
+            annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=tracked_detections)
+            annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=tracked_detections, labels=labels)
+            
+            for det in tracked_detections:
+                bbox, conf, class_id, tracker_id = det[0], det[2], int(det[3]), det[4]
+                results_list.append({
+                    "frame_number": frame_number,
+                    "track_id": int(tracker_id),
+                    "class": model.model.names[class_id],
+                    "confidence": float(conf),
+                    "bounding_box": [int(coord) for coord in bbox]
+                })
+            sink.write_frame(frame=annotated_frame)
+
+    with open(json_path, 'w') as f:
+        json.dump(results_list, f, indent=4)
+
+# --- API ENDPOINT ---
+@app.post("/track/")
+async def track_video_endpoint(video: UploadFile = File(...)):
+    # Use a temporary directory to handle file operations safely
+    with tempfile.TemporaryDirectory() as temp_dir:
+        input_path = os.path.join(temp_dir, video.filename)
+        output_video_path = os.path.join(temp_dir, "output.mp4")
+        output_json_path = os.path.join(temp_dir, "results.json")
+
+        # Save the uploaded video file
+        with open(input_path, "wb") as buffer:
+            buffer.write(await video.read())
+
+        # Run your existing processing logic
+        process_video_logic(input_path, output_video_path, output_json_path)
+
+        # Return the processed video as a downloadable file
+        return FileResponse(output_video_path, media_type="video/mp4", filename="output.mp4")

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:faae07aaf426488e2b959a4e54f5cf3a32b7b1fdc54c45b54b9937b732645a53
+size 6791092