app.py

from flask import send_from_directory
import os
import re
from dataclasses import dataclass
from typing import Dict, List, Tuple

import numpy as np
from flask import Flask, jsonify, render_template, request

import tensorflow as tf
from tensorflow.keras.models import load_model


# ----------------------------
# Model definitions
# ----------------------------

@dataclass(frozen=True)
class ModelSpec:
    id: str
    display_name: str          # what the user sees (friendly + technical)
    filename: str              # under ./models/
    arch: str                  # "resnet" | "efficientnet"
    img_size: int              # input resolution
    class_names: Tuple[str, ...]  # output order used during training
    recommended_threshold: float  # per-model uncertainty cutoff (from notebooks)


# NOTE:
# Your training notebooks use *different* class ordering between the ResNet notebooks
# (sorted unique categories) and the EfficientNet notebook (explicit list).
# We keep per-model class order to avoid mislabeling probabilities.
RESNET_CLASS_ORDER = ("MildDemented", "ModerateDemented", "NonDemented", "VeryMildDemented")
EFFICIENTNET_CLASS_ORDER = ("NonDemented", "VeryMildDemented", "MildDemented", "ModerateDemented")

MODEL_SPECS: List[ModelSpec] = [
    ModelSpec("atlas", "Atlas — ResNet-50", "resnet50.h5", "resnet", 224, RESNET_CLASS_ORDER, 0.95),
    ModelSpec("orion", "Orion — ResNet-101", "resnet101.h5", "resnet", 224, RESNET_CLASS_ORDER, 0.95),
    ModelSpec("pulse", "Pulse — EfficientNet-B2", "efficientnetb2.h5", "efficientnet", 260, EFFICIENTNET_CLASS_ORDER, 0.95),
]



# ----------------------------
# Flask app
# ----------------------------

app = Flask(__name__)

# Lazy-loaded models (load on first use). Keep only what we need in CPU Spaces.
_loaded_models: Dict[str, tf.keras.Model] = {}


def _get_spec(model_id: str) -> ModelSpec:
    for s in MODEL_SPECS:
        if s.id == model_id:
            return s
    raise KeyError(f"Unknown model_id: {model_id}")


def _get_preprocess_fn(arch: str):
    if arch == "resnet":
        from tensorflow.keras.applications.resnet50 import preprocess_input as resnet_preprocess
        return resnet_preprocess
    if arch == "efficientnet":
        from tensorflow.keras.applications.efficientnet import preprocess_input as eff_preprocess
        return eff_preprocess
    raise ValueError(f"Unknown arch: {arch}")


def _load_model(spec: ModelSpec) -> tf.keras.Model:
    if spec.id in _loaded_models:
        return _loaded_models[spec.id]

    model_path = os.path.join(os.path.dirname(__file__), "models", spec.filename)
    if not os.path.exists(model_path):
        raise FileNotFoundError(
            f"Model file not found: {model_path}. "
            f"Place it at models/{spec.filename} in your Space."
        )

    # CPU-friendly TF settings (small wins on free Spaces)
    try:
        tf.config.threading.set_intra_op_parallelism_threads(0)
        tf.config.threading.set_inter_op_parallelism_threads(0)
    except Exception:
        pass

    model = load_model(model_path, compile=False)
    _loaded_models[spec.id] = model
    return model


def _read_image(file_storage, img_size: int, preprocess_fn):
    # Decode image
    raw = file_storage.read()
    image = tf.io.decode_image(raw, channels=3, expand_animations=False)
    image = tf.image.resize(image, [img_size, img_size])
    image = tf.cast(image, tf.float32)
    image = preprocess_fn(image)
    image = tf.expand_dims(image, axis=0)  # [1, H, W, 3]
    return image


def _predict(model: tf.keras.Model, image_tensor, class_names: Tuple[str, ...], threshold: float):
    probs = model.predict(image_tensor, verbose=0)[0].astype(float)
    probs = np.clip(probs, 0.0, 1.0)

    best_idx = int(np.argmax(probs))
    best_prob = float(np.max(probs))

    # Add "Uncertain" post-hoc (not a model output class)
    is_uncertain = best_prob < threshold

    # Build response payload
    by_class = [
        {"id": name, "label": _pretty_label(name), "prob": float(probs[i])}
        for i, name in enumerate(class_names)
    ]
    by_class.sort(key=lambda x: x["prob"], reverse=True)

    return {
        "prediction": {
            "id": "Uncertain" if is_uncertain else class_names[best_idx],
            "label": "Uncertain" if is_uncertain else _pretty_label(class_names[best_idx]),
            "confidence": best_prob,
            "threshold": threshold,
        },
        "probabilities": by_class,
    }


def _pretty_label(name: str) -> str:
    # Internal training labels -> user-facing labels (final wording)
    mapping = {
        "NonDemented": "Healthy",
        "VeryMildDemented": "Very Mildly Demented",
        "MildDemented": "Mildly Demented",
        "ModerateDemented": "Moderately Demented",
        # Post-hoc
        "Uncertain": "Uncertain",
    }
    return mapping.get(name, name)


from flask import send_from_directory
import os

@app.route("/", defaults={"path": ""})
@app.route("/<path:path>")
def serve_frontend(path):
    # Keep API routes intact
    if path.startswith("api/"):
        return ("Not Found", 404)

    clarity_dir = os.path.join(app.root_path, "static", "clarity")
    requested = os.path.join(clarity_dir, path)

    # Serve real static files if they exist
    if path and os.path.isfile(requested):
        return send_from_directory(clarity_dir, path)

    # Otherwise serve React index.html
    return send_from_directory(clarity_dir, "index.html")



@app.get("/api/models")
def api_models():
    return jsonify({
        "models": [
            {
                "id": s.id,
                "name": s.display_name,
                "img_size": s.img_size,
                "classes": [{"id": c, "label": _pretty_label(c)} for c in s.class_names],
                "recommended_threshold": s.recommended_threshold,
            }
            for s in MODEL_SPECS
        ],
        "default_model_id": MODEL_SPECS[0].id,
    })



@app.post("/api/classify")
def api_classify():
    if "file" not in request.files:
        return jsonify({"error": "No file uploaded (field name must be 'file')."}), 400

    model_id = request.form.get("model_id", MODEL_SPECS[0].id)
    spec = _get_spec(model_id)
    # Threshold is model-specific and not user-adjustable
    threshold = spec.recommended_threshold

    try:
        model = _load_model(spec)
        preprocess_fn = _get_preprocess_fn(spec.arch)

        image_tensor = _read_image(request.files["file"], spec.img_size, preprocess_fn)
        payload = _predict(model, image_tensor, spec.class_names, threshold)

        payload["model"] = {"id": spec.id, "name": spec.display_name}
        return jsonify(payload)

    except FileNotFoundError as e:
        return jsonify({"error": str(e)}), 500
    except Exception as e:
        return jsonify({"error": f"Failed to classify image: {e}"}), 500


if __name__ == "__main__":
    # Local dev: python app.py
    # In Spaces (Dockerfile), gunicorn is used.
    app.run(host="0.0.0.0", port=int(os.getenv("PORT", "7860")), debug=False)