app.py

# The Complete and Final app.py with both a UI and an API

from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import gradio as gr
import tensorflow as tf
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import os
import base64
import io

# --- 1. Load the Model ---
model = None
try:
    model_path = hf_hub_download(
        repo_id="skibi11/leukolook-eye-detector", 
        filename="MobileNetV1_best.keras"
    )
    model = tf.keras.models.load_model(model_path)
    print("--- MODEL LOADED SUCCESSFULLY! ---")
except Exception as e:
    print(f"--- ERROR LOADING MODEL: {e} ---")
    model = None

# --- 2. Core Prediction Logic ---
def preprocess_image(img_pil):
    img = img_pil.resize((224, 224))
    img_array = np.array(img)
    if img_array.ndim == 2: img_array = np.stack((img_array,)*3, axis=-1)
    if img_array.shape[-1] == 4: img_array = img_array[..., :3]
    img_array = img_array / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

def run_prediction(pil_image):
    if model is None:
        return {"error": "Model is not loaded on the server."}
    
    processed_image = preprocess_image(pil_image)
    prediction = model.predict(processed_image)
    labels = [f"Class_{i}" for i in range(prediction.shape[1])]
    confidences = {label: float(score) for label, score in zip(labels, prediction[0])}
    return confidences

# --- 3. Create the FastAPI app ---
app = FastAPI()

# --- 4. Define the input data structure for our API endpoint ---
class PredictionRequest(BaseModel):
    data: list[str]

# --- 5. Create our reliable API endpoint for the Render backend ---
@app.post("/api/predict/")
async def handle_api_prediction(request: PredictionRequest):
    try:
        base64_string = request.data[0].split(',', 1)[1]
        image_bytes = base64.b64decode(base64_string)
        pil_image = Image.open(io.BytesIO(image_bytes))
        result_dict = run_prediction(pil_image)
        return JSONResponse(content={"data": [result_dict]})
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": str(e)})

# --- 6. Create the Gradio UI for the homepage ---
gradio_ui = gr.Interface(
    fn=run_prediction,
    inputs=gr.Image(type="pil", label="Upload an eye image to test"),
    outputs=gr.JSON(label="Prediction Results"),
    title="LeukoLook Eye Detector",
    description="A demonstration of the LeukoLook detection model. This UI can be used for direct testing."
)

# --- 7. Mount the Gradio UI onto the FastAPI app's root ---
app = gr.mount_gradio_app(app, gradio_ui, path="/")

# --- 8. To run the server ---
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)