app.py

# app.py
import os
import traceback
import tensorflow as tf
import numpy as np
from PIL import Image
import gradio as gr
from huggingface_hub import snapshot_download

# === CONFIG ===
MODEL_ID = "varshithkumar/wbc_resnet50"   # your model repo id
CLASS_NAMES = ['Basophil', 'Eosinophil', 'Lymphocyte', 'Monocyte', 'Neutrophil']

# Globals
model = None
infer = None   # serving function

# === Load model ===
def load_model():
    global infer
    hf_token = os.environ.get("HF_TOKEN")  # set in Space secrets if repo is private
    try:
        print(f"⏳ Downloading model from Hugging Face Hub: {MODEL_ID}")
        repo_dir = snapshot_download(repo_id=MODEL_ID, repo_type="model", token=hf_token)
        print("✅ Model snapshot downloaded at:", repo_dir)

        # Load TF SavedModel
        model = tf.saved_model.load(repo_dir)
        print("✅ Model loaded using tf.saved_model.load()")

        # Get serving function
        infer = model.signatures["serving_default"]

        # 🔍 Debug info
        print("Available signatures:", list(model.signatures.keys()))
        print("Serving function inputs:", infer.structured_input_signature)
        print("Serving function outputs:", infer.structured_outputs)

        return model
    except Exception as e:
        print("❌ Failed to load model:", e)
        traceback.print_exc()
        return None

model = load_model()
if model is None:
    print("WARNING: Model failed to load. Predictions will return an error.")

# === preprocessing & prediction ===
def preprocess_image(img: Image.Image):
    img = img.convert("RGB")
    img = img.resize((224, 224))  # ResNet50 expected input size
    arr = np.array(img).astype(np.float32) / 255.0
    arr = np.expand_dims(arr, 0)
    return arr

def predict(image):
    global infer
    if infer is None:
        return {"error": "Model not loaded. Check Space logs."}
    try:
        arr = preprocess_image(image)
        preds = infer(input_layer_2=tf.constant(arr))["output_0"].numpy()

        probs = preds[0].tolist()
        if len(probs) == len(CLASS_NAMES):
            out = {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
        else:
            out = {"class_" + str(i): float(p) for i, p in enumerate(probs)}
        return out
    except Exception as e:
        print("Prediction error:", e)
        traceback.print_exc()
        return {"error": str(e)}

# === Gradio UI ===
title = "WBC ResNet50 - White Blood Cell Classifier"
description = "Upload a blood-smear image. Model resizes input to 224×224. If model fails to load, predictions will error."

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload image"),
    outputs=gr.Label(num_top_classes=None, label="Predictions"),
    title=title,
    description=description,
    allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch(show_error=True)  # ✅ enable verbose error reporting