add

Dockerfile

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+# Stage 1: Build stage
+FROM python:3.10-slim AS build
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
+
+COPY . .
+
+# Stage 2: Production stage
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Copy installed packages from build stage
+COPY --from=build /install /usr/local
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+import os
+from fastapi import File, UploadFile
+import numpy as np
+from PIL import Image
+
+repo_id = "Sathvika-Alla/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 = FastAPI()
+
+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.
+
+
+@app.post('/upload/image')
+async def uploadImage(img: UploadFile = File(...)):
+    original_image = Image.open(img.file) # Read the bytes and process as an image
+    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"