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model.py

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+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load your trained CNN model
+model = tf.keras.models.load_model("saved_model/Inception_V3_Animals_Classification.h5")
+
+# Same label order you used when training (from LabelEncoder)
+CLASS_NAMES = ["Cat", "Dog", "Snake"]
+
+def preprocess_image(img: Image.Image, target_size=(256, 256)):
+    """
+    Preprocess a PIL image to match training pipeline:
+    - Convert to RGB
+    - Resize
+    - Convert to float32
+    - Normalize to [0,1]
+    - Add batch dimension
+    """
+    img = img.convert("RGB")  # ensure 3 channels
+    img = img.resize(target_size)
+    img = np.array(img).astype("float32") / 255.0  # normalize
+    img = np.expand_dims(img, axis=0)  # (1, 256, 256, 3)
+    return img
+
+def predict(img: Image.Image):
+    # Apply preprocessing
+    input_tensor = preprocess_image(img)
+
+    # Model prediction
+    preds = model.predict(input_tensor)
+    probs = preds[0]
+
+    class_idx = int(np.argmax(probs))
+    confidence = float(np.max(probs))
+
+    # Map all probabilities
+    prob_dict = {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
+
+    return CLASS_NAMES[class_idx], confidence, prob_dict

requirements.txt

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+fastapi
+uvicorn
+tensorflow
+numpy
+python-multipart
+pillow

saved_model/Inception_V3_Animals_Classification.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:36279af47b3e49f968c806e51198d42428d155a4ab8bd9966581218ff2aa2252
+size 142072584