Add application file

Dockerfile

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+# ------------------------------------------------------------
+# Base image
+# ------------------------------------------------------------
+FROM python:3.12-slim-bookworm
+
+# ------------------------------------------------------------
+# Environment
+# ------------------------------------------------------------
+#   PYTHONUNBUFFERED  : ensures stdout/stderr aren’t buffered (logs show up instantly)
+#   PYTHONDONTWRITEBYTECODE : avoids .pyc files
+#   PIP_NO_CACHE_DIR  : prevents pip from caching wheels
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1
+
+    # Create a dedicated folder for Ultralytics settings
+RUN mkdir -p /app/config
+
+# Set the environment variable to point to that folder
+ENV YOLO_CONFIG_DIR=/app/config
+# ------------------------------------------------------------
+# Working directory inside container
+# ------------------------------------------------------------
+WORKDIR /app
+
+# ------------------------------------------------------------
+# Copy application files
+# ------------------------------------------------------------
+# Copy only what’s required first (so Docker can cache layers)
+
+COPY . .
+
+# ------------------------------------------------------------
+# System + Python dependencies
+# ------------------------------------------------------------
+# --no-install-recommends  : install only what we ask for
+# Remove apt cache & docs to reduce size
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        ffmpeg \
+        libgl1 \
+        libglib2.0-0 \
+        libsm6 \
+        libxrender1 \
+        libxext6 && \
+    rm -rf /var/lib/apt/lists/* /usr/share/doc /usr/share/man /usr/share/locale && \
+    \
+    # Upgrade pip & install Python dependencies (CPU-only Torch)
+    pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir \
+        torch==2.4.1+cpu \
+        torchvision==0.19.1+cpu \
+        --index-url https://download.pytorch.org/whl/cpu && \
+    pip install --no-cache-dir -r requirements.txt && \
+    # Editable install so the package can be imported elsewhere
+    pip install --no-cache-dir -e .
+
+# ------------------------------------------------------------
+# Networking
+# ------------------------------------------------------------
+EXPOSE 8000
+
+# ------------------------------------------------------------
+# Start FastAPI with auto-reload (remove --reload for production)
+# ------------------------------------------------------------
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--reload"]

app.py

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+import os
+import uuid
+import tempfile
+import pandas as pd
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import FileResponse
+import uvicorn
+
+from backend.services.video_data_extraction.video_preprocessor import VideoDataExtractor
+from backend.services.prediction.predictor import ViolencePredictor
+
+app = FastAPI(title="Video Analysis Backend")
+
+processor = VideoDataExtractor()
+predictor = ViolencePredictor()
+jobs: dict[str, dict] = {}
+
+
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "ok", "message": "Service is running"}
+
+
+@app.post("/process-video/")
+async def process_video(file: UploadFile = File(...)):
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as input_video:
+            input_video.write(await file.read())
+            input_path = input_video.name
+
+        output_csv = tempfile.NamedTemporaryFile(delete=False, suffix=".csv").name
+        output_video_path = tempfile.NamedTemporaryFile(
+            delete=False, suffix=".mp4"
+        ).name
+
+        frame_w, frame_h, num_interactions = processor.extract_video_data(
+            input_path,
+            output_csv,
+            output_folder=os.path.dirname(output_video_path),
+            save_video=True,
+        )
+
+        job_id = str(uuid.uuid4())
+        jobs[job_id] = {"csv": output_csv, "video": output_video_path}
+
+        return {
+            "job_id": job_id,
+            "message": f"Processed video with {num_interactions} interactions",
+            "frame_width": frame_w,
+            "frame_height": frame_h,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        if os.path.exists(input_path):
+            os.unlink(input_path)
+
+
+@app.get("/get-results/{job_id}")
+async def get_results(job_id: str):
+    if job_id not in jobs:
+        raise HTTPException(status_code=404, detail="Job ID not found")
+    csv_path = jobs[job_id]["csv"]
+    if not os.path.exists(csv_path):
+        raise HTTPException(status_code=404, detail="CSV file not found")
+    return FileResponse(
+        csv_path, media_type="text/csv", filename="violence_analysis_results.csv"
+    )
+
+
+@app.get("/sample-results/{job_id}")
+async def get_sample_results(job_id: str):
+    if job_id not in jobs:
+        raise HTTPException(status_code=404, detail="Job ID not found")
+    csv_path = jobs[job_id]["csv"]
+    if not os.path.exists(csv_path):
+        raise HTTPException(status_code=404, detail="CSV file not found")
+    df = pd.read_csv(csv_path)
+    return df.head(5).to_dict(orient="records")
+
+
+@app.post("/predict/{job_id}")
+async def predict_violence(job_id: str):
+    if job_id not in jobs:
+        raise HTTPException(status_code=404, detail="Job ID not found")
+    csv_path = jobs[job_id]["csv"]
+    if not os.path.exists(csv_path):
+        raise HTTPException(status_code=404, detail="CSV file not found")
+    df = pd.read_csv(csv_path)
+    preds = predictor.predict(df)
+    return {"predictions": preds.tolist()}
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn[standard]
+numpy
+opencv-python
+pandas
+scikit-learn
+python-multipart
+# any other dependencies your backend uses