app.py

import tensorflow as tf
import gradio as gr
import numpy as np
from huggingface_hub import hf_hub_download

# 1) download your SavedModel from the Hub
repo_id = "Alessia2004/animal-cnn-model"
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")

# 3) simple preprocess + predict
CLASS_NAMES = ["cat","dog","panda"]
def predict(image):
    resized_image = tf.image.resize(image, (64,64))
    images_to_predict = np.expand_dims(np.array(resized_image), axis=0)
    probs = model.predict(images_to_predict)[0]
    return {c: float(p) for c,p in zip(CLASS_NAMES, probs)}

# 4) launch Gradio
# gr.Interface(
#     fn=predict, 
#     inputs=gr.Image(), 
#     outputs=gr.Label(num_top_classes=3)
# ).launch()

# 4) Launch Gradio interface; API will be automatically enabled in HF Spaces.
gr.Interface(
    fn=predict, 
    inputs=gr.Image(), 
    outputs=gr.Label(num_top_classes=3)
).launch()


# import tensorflow as tf
# import gradio as gr
# import numpy as np
# from huggingface_hub import hf_hub_download

# # 1) download your SavedModel from the Hub
# repo_id = "Alessia2004/animal-cnn-model"
# 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")

# # 3) simple preprocess + predict
# CLASS_NAMES = ["cat", "dog", "panda"]
# def predict(image):
#     resized_image = tf.image.resize(image, (64, 64))
#     images_to_predict = np.expand_dims(np.array(resized_image), axis=0)
#     probs = model.predict(images_to_predict)[0]
#     return {c: float(p) for c, p in zip(CLASS_NAMES, probs)}

# # 4) Launch Gradio interface with JSON output for API
# gr.Interface(
#     fn=predict, 
#     inputs=gr.Image(type="numpy"),  # Ensure image is processed as numpy array
#     outputs="json"  # Return raw JSON response for API calls
# ).launch()