Upload 4 files

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+ENV MODEL_PATH=/app/generator_final.h5
+
+WORKDIR /app
+
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy model and app files
+COPY --chown=user generator_final.h5 .
+COPY --chown=user app.py .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse, StreamingResponse
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+import io
+import os
+
+app = FastAPI(title="GAN Image Generator API")
+
+# Load model
+model_path = os.getenv("MODEL_PATH", "generator_final.h5")
+generator = load_model(model_path)
+
+def generate_image(noise_dim=100):
+    """Generate image from random noise"""
+    z = np.random.randn(1, noise_dim, 1, 1).astype(np.float32)
+    fake_image = generator.predict(z)
+    fake_image = (fake_image.squeeze() * 255).astype(np.uint8)
+    return fake_image[..., :3]
+
+@app.get("/")
+def read_root():
+    return {"message": "GAN Image Generator API"}
+
+@app.get("/generate-random")
+async def generate_random_image():
+    """Endpoint to generate random image"""
+    image_array = generate_image()
+    img = Image.fromarray(image_array)
+    
+    # Convert to bytes
+    img_byte_arr = io.BytesIO()
+    img.save(img_byte_arr, format='PNG')
+    img_byte_arr.seek(0)
+    
+    return StreamingResponse(img_byte_arr, media_type="image/png")
+
+@app.post("/generate-from-sketch")
+async def generate_from_sketch(file: UploadFile = File(...)):
+    """Endpoint to generate from sketch"""
+    # Process your sketch here (add your sketch processing logic)
+    # For now just returns a random image
+    image_array = generate_image()
+    img = Image.fromarray(image_array)
+    
+    img_byte_arr = io.BytesIO()
+    img.save(img_byte_arr, format='PNG')
+    img_byte_arr.seek(0)
+    
+    return StreamingResponse(img_byte_arr, media_type="image/png")

generator_final.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3536d9341014426b6488e885ac4574b96049663ed949b63298c46b7a7cce87b8
+size 217820864

requirements.txt

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+torch>=2.0.0
+torchvision>=0.15.0
+gradio>=4.0.0
+numpy>=1.24.0
+tensorflow>=2.10
+fastapi
+uvicorn[standard]
+tensorflow
+pillow
+numpy
+python-multipart