Upload 5 files

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.9-slim
+
+WORKDIR /app
+
+# Copy requirement dan install dulu (biar cache Docker efisien)
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy semua file lain (model, kode Python, dll)
+COPY . .
+
+# Jalankan Uvicorn langsung
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]

app.py

@@ -0,0 +1,46 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+import numpy as np
+import io
+
+# Initialize FastAPI app
+app = FastAPI(title="Cat vs Dog Classifier API")
+
+# Load the pre-trained model
+model = tf.keras.models.load_model('model.h5')
+
+# Define class labels
+class_names = ['Cat', 'Dog']
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    # Check if the uploaded file is an image
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File must be an image")
+
+    # Read and preprocess the image
+    try:
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents)).convert('RGB')
+        image = image.resize((224, 224))  # Resize to match model input
+        image_array = img_to_array(image) / 255.0  # Rescale to [0, 1]
+        image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+
+        # Make prediction
+        prediction = model.predict(image_array)
+        predicted_class = class_names[int(prediction[0][0] > 0.5)]  # Sigmoid threshold
+        confidence = float(prediction[0][0]) if predicted_class == 'Dog' else float(1 - prediction[0][0])
+
+        return JSONResponse({
+            "predicted_class": predicted_class,
+            "confidence": confidence
+        })
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Cat vs Dog Classifier API. Use POST /predict/ to classify an image."}

labels.txt

@@ -0,0 +1,2 @@
+cats: 0
+dogs: 1

model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db6ea3352d113127c110573ee48d87b912ade913fa7ba0fa63a765ff9ababcaf
+size 25326488

requirements.txt

@@ -0,0 +1,5 @@
+fastapi
+uvicorn
+tensorflow
+pillow
+numpy