remove Docker files

Dockerfile

@@ -1,15 +0,0 @@
-# Dockerfile
-
-FROM python:3.11-slim
-
-# Install dependencies
-RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-CMD ["python", "app.py"]

app/main.py

@@ -1,90 +0,0 @@
-# app/main.py
-
-import os
-from fastapi import FastAPI, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
-from PIL import Image
-import torch
-from torchvision import transforms as T
-from src.train import loss_fn  # Load loss function from src/train.py
-from src.model_config import load_model, set_device
-import io
-from io import BytesIO
-
-import base64
-import matplotlib.pyplot as plt
-
-import logging
-log = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
-
-app = FastAPI()
-
-device = set_device()
-log.info("Using device: %s", device)
-model = load_model(device=device)
-log.info("Model loaded. App is ready.")
-
-model.eval()
-
-# Transformations
-transform = T.Compose([
-    T.Grayscale(num_output_channels=1),
-    T.Resize((28, 28)),
-    T.ToTensor()
-])
-
-@app.get("/health")
-def health():
-    return {"status": "ok"}
-
-@app.post("/predict", summary="Predict anomaly score from image")
-async def predict(file: UploadFile):
-    try:
-        contents = await file.read()
-        image = Image.open(io.BytesIO(contents)).convert("L")
-        image = transform(image)
-        image = image.unsqueeze(0).to(device)
-
-        with torch.no_grad():
-            decoded = model(image)
-            score = loss_fn(decoded, image).item()
-            diff = (decoded - image).squeeze().cpu().numpy() ** 2
-
-        # Create heatmap image
-        fig, ax = plt.subplots()
-        ax.imshow(diff, cmap="hot")
-        ax.axis("off")
-        buf = BytesIO()
-        plt.savefig(buf, format="png", bbox_inches="tight", pad_inches=0)
-        plt.close(fig)
-        buf.seek(0)
-
-        # Encode to base64
-        heatmap_b64 = base64.b64encode(buf.read()).decode("utf-8")
-
-        # Create reconstructed image
-        decoded_img = decoded.squeeze().cpu().numpy()
-
-        # Visualize the decoded image as png
-        fig2, ax2 = plt.subplots()
-        ax2.imshow(decoded_img, cmap="gray")
-        ax2.axis("off")
-        buf2 = BytesIO()
-        plt.savefig(buf2, format="png", bbox_inches="tight", pad_inches=0)
-        plt.close(fig2)
-        buf2.seek(0)
-
-        # Decode to base64
-        decoded_b64 = base64.b64encode(buf2.read()).decode("utf-8")
-
-        # Return everything as JSON
-        return JSONResponse(content={
-            "anomaly_score": score,
-            "heatmap": heatmap_b64,
-            "decoded_image": decoded_b64
-        })
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Unexpected error: {str(e)}")
-

docker-compose.yml

@@ -1,12 +0,0 @@
-version: "3.8"
-
-services:
-  frontend:
-    build: .
-    ports:
-      - "7860:7860"
-    volumes:
-      - .:/app
-    environment:
-      - PYTHONUNBUFFERED=1
-    restart: unless-stopped