initial commit

.dockerignore

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+# Ignore the AI model directory
+animal-classification/
+
+# Ignore Python virtual environments and cache
+venv/
+__pycache__/
+*.pyc
+
+# Ignore Git and other development artifacts
+.git/
+.vscode/

Dockerfile

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+# --- Builder Stage ---
+# Use a full Python image to build dependencies
+FROM python:3.12 as builder
+
+WORKDIR /app
+
+# Install dependencies into a virtual environment
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# --- Runner Stage ---
+# Use a slim image for the final application
+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Copy the virtual environment from the builder stage
+COPY --from=builder /opt/venv /opt/venv
+
+# Copy the application code
+COPY . .
+
+# Set the path to use the virtual environment
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Expose the port and run the app
+EXPOSE 8000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

compose.yaml

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+version: '3.8'
+services:
+  app:
+    build: .
+    ports:
+      - "8000:8000"
+    volumes:
+      # Mount the local model directory into the container at the path the app expects
+      - ./animal-classification:/app/animal-classification

main.py

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+import uvicorn
+
+
+import fastapi
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi import File, UploadFile
+import numpy as np
+from PIL import Image
+
+
+from typing import Any, Dict
+import os
+import pkgutil
+print('starlette', __import__('starlette').__version__)
+
+from huggingface_hub import hf_hub_download
+from typing import Any, Dict
+
+import tensorflow as tf
+print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
+if tf.config.list_physical_devices('GPU'):
+    print(tf.config.list_physical_devices('GPU')[0].device_type, tf.config.list_physical_devices('GPU')[0].name )
+
+from tensorflow import keras
+TF_AVAILABLE = True
+print("tensorflow version: ", tf.__version__)
+print("keras version: ", keras.__version__)
+
+
+# starlette is a FastAPI dependency; import if available
+try:
+	import starlette
+except Exception:
+	starlette = None
+
+# TensorFlow can be large or absent in some envs; guard the import so
+# importing this module doesn't crash tests or other tooling.
+try:
+	import tensorflow as tf
+	from tensorflow import keras
+	TF_AVAILABLE = True
+	print("tensorflow version: ", tf.__version__)
+	print("keras version: ", keras.__version__)
+except Exception:
+	tf = None
+	keras = None
+	TF_AVAILABLE = False
+	print("tensorflow not available")
+
+
+app = FastAPI(title="1.3 - AI Model Deployment")
+''' browser: http://localhost:8000/docs'''
+
+from fastapi.middleware.cors import CORSMiddleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+
+
+
+
+ANIMALS = ['Cat', 'Dog', 'Panda'] # Animal names here, these represent the labels of the images that we trained our model on.
+
+# 1) download your SavedModel from the Hub:
+# so refer to the repository where your model is, not the one for the space! 
+repo_id = "IDS75912/masterclass-2025"
+hf_hub_download(repo_id, filename="config.json", repo_type="model", local_dir="./model")
+hf_hub_download(repo_id, filename="metadata.json", repo_type="model", local_dir="./model")
+hf_hub_download(repo_id, filename="model.weights.h5", repo_type="model", local_dir="./model")
+
+# 2) load it
+model = tf.keras.models.load_model("./model")
+
+
+
+
+
+@app.post('/upload/image')
+async def uploadImage(img: UploadFile = File(...)):
+    original_image = Image.open(img.file) # Read the bytes and process as an image
+    if original_image.mode == 'RGBA':
+        original_image = original_image.convert('RGB')
+    resized_image = original_image.resize((64, 64)) # Resize
+    images_to_predict = np.expand_dims(np.array(resized_image), axis=0) # Our AI Model wanted a list of images, but we only have one, so we expand it's dimension
+    predictions = model.predict(images_to_predict) # The result will be a list with predictions in the one-hot encoded format: [ [0 1 0] ]
+    prediction_probabilities = predictions
+    classifications = prediction_probabilities.argmax(axis=1) # We try to fetch the index of the highest value in this list [ [1] ]
+
+    return ANIMALS[classifications.tolist()[0]] # Fetch the first item in our classifications array, format it as a list first, result will be e.g.: "Dog"
+
+@app.get("/")
+def read_root() -> Dict[str, Any]:
+    """Root endpoint."""
+    return {"message": "Hello from FastAPI in the 'aai9' conda env"}
+
+
+# @app.post("/echo")
+# async def echo(payload: Dict[str, Any]) -> Dict[str, Any]:
+#     """Echo back the received JSON payload."""
+#     return {"echo": payload}
+
+
+@app.get("/version")
+def versions() -> Dict[str, Any]:
+    """Return key package versions and whether TensorFlow is available."""
+    return {
+        "fastapi": fastapi.__version__,
+        "starlette": getattr(starlette, "__version__", None),
+        "tensorflow_available": TF_AVAILABLE,
+        "tensorflow_version": getattr(tf, "__version__", None),
+    }
+
+
+@app.get("/predict")
+def predict_stub() -> Dict[str, Any]:
+    """A tiny predict stub that demonstrates how to expose model inference.
+
+    If TensorFlow isn't available or no model is loaded this returns a helpful
+    message.
+    """
+    if not TF_AVAILABLE or model is None:
+        return {
+            "prediction": "N/A",
+            "info": "TensorFlow not available or model not loaded.",
+        }
+    # This is a stub, so we're not doing a real prediction
+    return {"prediction": "stub, we're not doing a real prediction", "model_path": model_path}
+
+
+
+
+
+
+if __name__ == "__main__":
+	# Run with: conda run -n gradio uvicorn main:app --reload
+	import uvicorn
+
+	uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

requirements.txt

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+absl-py==2.3.1
+annotated-types==0.6.0
+anyio==4.10.0
+asttokens==3.0.0
+astunparse==1.6.3
+bottleneck==1.4.2
+certifi==2025.10.5
+charset-normalizer==3.4.3
+click==8.2.1
+comm==0.2.3
+debugpy==1.8.16
+decorator==5.2.1
+exceptiongroup==1.3.0
+executing==2.2.1
+fastapi==0.116.1
+flatbuffers==25.9.23
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.75.1
+h11==0.16.0
+h5py==3.14.0
+idna==3.10
+importlib-metadata==8.7.0
+ipykernel==6.30.1
+ipython==9.6.0
+ipython_pygments_lexers==1.1.1
+jedi==0.19.2
+jupyter-client==8.6.3
+jupyter-core==5.8.1
+keras==3.11.3
+libclang==18.1.1
+markupsafe==3.0.3
+markdown==3.9
+markdown-it-py==4.0.0
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mkl-service==2.5.2
+mkl_fft==1.3.11
+mkl_random==1.2.8
+ml-dtypes==0.5.3
+namex==0.1.0
+nest-asyncio==1.6.0
+numexpr==2.11.0
+numpy
+opt-einsum==3.4.0
+optree==0.17.0
+packaging==25.0
+pandas==2.3.3
+parso==0.8.5
+pexpect==4.9.0
+pickleshare==0.7.5
+pillow==11.3.0
+pip==25.2
+platformdirs==4.5.0
+prompt-toolkit==3.0.52
+protobuf==6.32.1
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pydantic==2.11.9
+pydantic-core==2.33.2
+pygments==2.19.2
+python-dateutil==2.9.0post0
+python-multipart==0.0.20
+pytz==2025.2
+pyzmq==27.1.0
+requests==2.32.5
+rich==14.2.0
+setuptools==72.1.0
+six==1.17.0
+sniffio==1.3.0
+stack_data==0.6.3
+starlette==0.47.3
+tensorboard==2.20.0
+tensorboard-data-server==0.7.2
+tensorflow==2.20.0
+termcolor==3.1.0
+tornado==6.5.1
+traitlets==5.14.3
+typing-extensions==4.15.0
+typing-inspection==0.4.0
+urllib3==2.5.0
+uvicorn==0.35.0
+wcwidth==0.2.14
+werkzeug==3.1.3
+wheel==0.45.1
+wrapt==1.17.3
+zipp==3.23.0
+gradio
+huggingface_hub