app.py

import os
import numpy as np
import gradio as gr
import cv2
import tensorflow as tf
import keras
from keras.models import Model
from keras.preprocessing import image
from huggingface_hub import hf_hub_download
import pandas as pd
from PIL import Image
import plotly.express as px
import time

theme = gr.themes.Soft(
    primary_hue="purple",
    secondary_hue="yellow",
    text_size="sm",
)

css = """
#warning {background-color: #FFCCCB}

/* Surbrillance pour "Malin" (rouge #F54927) */
.highlight.malin {
    background-color: #F54927;
    color: white;
    font-weight: bold;
    padding: 10px 6px;
    border-radius: 4px;
    font-size:24px;
}
#.highlight.malin h2.output-class {
#    font-size: 24px !important;  /* Cible le <h2> à l'intérieur du span */
#}

/* Surbrillance pour "Bénin" (vert #34EA3A) */
.highlight.benin {
    background-color: #34EA3A;
    color: black;
    font-weight: bold;
    padding: 3px 6px;
    border-radius: 4px;
    font-size:24px;
}
#.highlight.benin h2.output-class {
#    font-size: 24px !important;  /* Optionnel : si tu veux aussi appliquer à "Bénin" */
#}

/* Style pour le Diagnostic global */
.diagnostic-global h2.output-class {
    font-size: 24px !important;
}

/* Style pour l'explication */
.feedback h2.output-class {
    font-size: 16px !important;
}
"""



# Désactiver GPU et logs TensorFlow
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.config.set_visible_devices([], 'GPU')

# ---- Configuration ----
CLASS_NAMES = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']
label_to_index = {name: i for i, name in enumerate(CLASS_NAMES)}
diagnosis_map = {
    'akiec': 'Bénin', 'bcc': 'Malin', 'bkl': 'Bénin', 'df': 'Bénin',
    'nv': 'Bénin', 'vasc': 'Bénin', 'mel': 'Malin'
}
description = {
    "akiec": "Bénin : AKIEC ou kératoses solaires sont des excroissances précancéreuses provoquées par l'exposition solaire prolongée. Le risque de progression d'une Kératose Actinique vers un carcinome épidermoïde (cancer de la peau non mélanome) existe mais reste modéré",
    "bcc": "Malin : BCC ou carcinome basocellulaire est un type de cancer cutané. C’est le cancer de la peau le plus fréquent. Il se manifeste par la formation d'une masse, d'un bouton ou d'une lésion sur la couche externe de la peau.",
    "bkl": "Bénin : BKL ou kératose séborrhéique se présente sous la forme d’une lésion pigmentée, en relief, verruqueuse (qui ressemble à une verrue), souvent croûteuse, de plus ou moins grande taille.",
    "df": "Bénin : DF ou dermatofibrome est une lésion papuleuse ou nodulaire ferme, le plus souvent de petite taille, de couleur rouge marron, de nature fibrohistiocytaire.",
    "nv": "Bénin : NV (Nevus) ou le grain de beauté, couramment appelés nevus mélanocytaires représentent une accumulation localisée de mélanocytes dans la peau",
    "vasc": "Bénin : VASC ou "lésion vasculaire" se traduit par la survenue d’anomalies visibles en surface de la peau et d’aspect variable : rougeurs, taches planes ou en relief, capillaires sanguins apparents",
    "mel": "Malin : MEL ou Mélanome est un nodule noir ou couleur « peau » présent sur n'importe quelle partie de la peau. Sa consistance est ferme et le nodule peut s'ulcérer, se couvrir d'une croûte, suinter ou saigner."
}

# ---- Chargement des modèles ----
def load_models_safely():
    models = {}
    try:
        print("📥 Téléchargement ResNet50...")
        resnet_path = hf_hub_download(repo_id="ericjedha/resnet50", filename="Resnet50.keras")
        models['resnet50'] = keras.saving.load_model(resnet_path, compile=False)
        print("✅ ResNet50 chargé")
    except Exception as e:
        models['resnet50'] = None
    
    try:
        print("📥 Téléchargement DenseNet201...")
        densenet_path = hf_hub_download(repo_id="ericjedha/densenet201", filename="Densenet201.keras")
        models['densenet201'] = keras.saving.load_model(densenet_path, compile=False)
        print("✅ DenseNet201 chargé")
    except Exception as e:
        models['densenet201'] = None
    
    try:
        print("📥 Chargement Xception local...")
        if os.path.exists("Xception.keras"):
            models['xception'] = keras.saving.load_model("Xception.keras", compile=False)
            print("✅ Xception chargé")
        else:
            models['xception'] = None
    except Exception as e:
        models['xception'] = None
    
    loaded = {k: v for k, v in models.items() if v is not None}
    if not loaded:
        raise Exception("❌ Aucun modèle n'a pu être chargé!")
    
    print(f"🎯 Modèles chargés: {list(loaded.keys())}")
    return models

try:
    models_dict = load_models_safely()
    model_resnet50 = models_dict.get('resnet50')
    model_densenet = models_dict.get('densenet201') 
    model_xcept = models_dict.get('xception')
except Exception as e:
    print(f"🚨 ERREUR CRITIQUE: {e}")
    model_resnet50 = model_densenet = model_xcept = None

# ---- Préprocesseurs ----
from tensorflow.keras.applications.xception import preprocess_input as preprocess_xception
from tensorflow.keras.applications.resnet50 import preprocess_input as preprocess_resnet
from tensorflow.keras.applications.densenet import preprocess_input as preprocess_densenet

# ---- Utils ----
def _renorm_safe(p: np.ndarray) -> np.ndarray:
    p = np.clip(p, 0.0, None)  # Évite les valeurs négatives
    s = np.sum(p)
    if s <= 0:
        return np.ones_like(p, dtype=np.float32) / len(p)
    normalized = p / s
    return normalized / np.sum(normalized) if np.sum(normalized) > 1.0001 else normalized

def get_primary_input_name(model):
    if isinstance(model.inputs, list) and len(model.inputs) > 0:
        return model.inputs[0].name.split(':')[0]
    return "input_1" 

def _update_progress(progress, value, desc=""):
    """
    Met à jour la barre de progression.
    """
    if progress is not None:
        progress(value / 100.0, desc=desc)

# ---- PREDICT SINGLE ----
def predict_single(img_input, weights=(0.45, 0.25, 0.30), normalize=True):
    if isinstance(img_input, str):
        pil_img = Image.open(img_input).convert("RGB")
    elif isinstance(img_input, Image.Image):
        pil_img = img_input.convert("RGB")
    else:
        raise ValueError("img_input doit être un chemin (str) ou une image PIL")

    preds = {}
    if model_xcept is not None:
        img_x = np.expand_dims(preprocess_xception(np.array(pil_img.resize((299, 299), resample=Image.BILINEAR))), axis=0)
        preds['xception'] = model_xcept.predict(img_x, verbose=0)[0]
    if model_resnet50 is not None:
        img_r = np.expand_dims(preprocess_resnet(np.array(pil_img.resize((224, 224), resample=Image.BILINEAR))), axis=0)
        preds['resnet50'] = model_resnet50.predict(img_r, verbose=0)[0]
    if model_densenet is not None:
        img_d = np.expand_dims(preprocess_densenet(np.array(pil_img.resize((224, 224), resample=Image.BILINEAR))), axis=0)
        preds['densenet201'] = model_densenet.predict(img_d, verbose=0)[0]

    ensemble = np.zeros(len(CLASS_NAMES), dtype=np.float32)
    if 'xception' in preds: ensemble += weights[0] * preds['xception']
    if 'resnet50' in preds: ensemble += weights[1] * preds['resnet50']
    if 'densenet201' in preds: ensemble += weights[2] * preds['densenet201']
    if 'densenet201' in preds:
        mel_idx = label_to_index['mel']
        ensemble[mel_idx] = 0.5 * ensemble[mel_idx] + 0.5 * preds['densenet201'][mel_idx]

    if normalize:
        ensemble = _renorm_safe(ensemble)

    preds['ensemble'] = ensemble
    return preds

# ---- Helpers Grad-CAM ----
LAST_CONV_LAYERS = {
    "xception": "block14_sepconv2_act", 
    "resnet50": "conv5_block3_out", 
    "densenet201": "conv5_block32_concat"
}

def find_last_dense_layer(model):
    for layer in reversed(model.layers):
        if isinstance(layer, keras.layers.Dense):
            return layer
    raise ValueError("Aucune couche Dense trouvée dans le modèle.")

# ---- GRAD-CAM AVEC PROGRESSION OPTIMISÉE ----
def make_gradcam(image_pil, model, last_conv_layer_name, class_index, progress=None):
    """
    Grad-CAM avec progression fluide grâce aux micro-pauses
    """
    if model is None: 
        return np.array(image_pil)
    
    try:
        steps = [
            (5, "🔄 Initialisation..."),
            (10, "🖼️ Analyse de l'image..."),
            (15, "⚙️ Configuration du preprocesseur..."),
            (20, "📐 Redimensionnement image..."),
            (25, "🧠 Configuration du modèle..."),
            (30, "🔗 Création du gradient model..."),
            (35, "⚡ Préparation du calcul..."),
            (40, "🔥 Forward pass..."),
            (45, "📊 Calcul des activations..."),
            (50, "🎯 Extraction classe cible..."),
            (55, "⚡ Calcul du gradient..."),
            (60, "📈 Traitement des gradients..."),
            (70, "📊 Pooling des gradients..."),
            (75, "🎨 Construction heatmap..."),
            (80, "🌡️ Normalisation heatmap..."),
            (85, "🎯 Application colormap..."),
            (90, "🖼️ Redimensionnement final..."),
            (95, "✨ Superposition images..."),
            (100, "✅ Terminé !")
        ]

        step = 0
        def next_step():
            nonlocal step
            if step < len(steps):
                val, desc = steps[step]
                _update_progress(progress, val, desc)
                time.sleep(0.02)  # Micro-pause pour permettre la mise à jour
                step += 1

        next_step()  # 5% - Initialisation

        # Détermination de la taille d'entrée et du preprocesseur
        input_size = model.input_shape[1:3]
        if 'xception' in model.name.lower(): 
            preprocessor = preprocess_xception
        elif 'resnet50' in model.name.lower(): 
            preprocessor = preprocess_resnet
        elif 'densenet' in model.name.lower():
            preprocessor = preprocess_densenet
        else:
            preprocessor = preprocess_densenet

        next_step()  # 10% - Analyse image
        next_step()  # 15% - Config preprocesseur

        # Préparation de l'image
        img_np = np.array(image_pil.convert("RGB"))
        img_resized = cv2.resize(img_np, input_size)
        img_array_preprocessed = preprocessor(np.expand_dims(img_resized, axis=0))

        next_step()  # 20% - Redimensionnement
        next_step()  # 25% - Config modèle

        # Configuration du modèle pour Grad-CAM
        try:
            conv_layer = model.get_layer(last_conv_layer_name)
        except ValueError:
            return img_resized

        grad_model = Model(model.inputs, [conv_layer.output, model.output])
        input_name = get_primary_input_name(model)
        input_for_model = {input_name: img_array_preprocessed}

        next_step()  # 30% - Gradient model
        next_step()  # 35% - Préparation calcul
        next_step()  # 40% - Forward pass

        # Le calcul critique avec étapes intermédiaires
        with tf.GradientTape() as tape:
            next_step()  # 45% - Calcul activations
            
            last_conv_layer_output, preds = grad_model(input_for_model, training=False)
            
            next_step()  # 50% - Extraction classe
            
            if isinstance(preds, list): 
                preds = preds[0]
            class_channel = preds[:, int(class_index)]

        next_step()  # 55% - Calcul gradient

        grads = tape.gradient(class_channel, last_conv_layer_output)
        if grads is None:
            return img_resized

        next_step()  # 60% - Traitement gradients
        next_step()  # 70% - Pooling

        # Pooling des gradients
        pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
        last_conv_layer_output = last_conv_layer_output[0]

        next_step()  # 75% - Construction heatmap

        # Construction de la heatmap
        heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
        heatmap = tf.squeeze(heatmap)
        heatmap = tf.maximum(heatmap, 0)
        max_val = tf.math.reduce_max(heatmap)
        
        if max_val == 0:
            heatmap = tf.ones_like(heatmap) * 0.5
        else:
            heatmap = heatmap / max_val

        next_step()  # 80% - Normalisation
        next_step()  # 85% - Colormap

        # Conversion et application du colormap
        heatmap_np = heatmap.numpy()
        heatmap_np = np.clip(heatmap_np.astype(np.float32), 0, 1)

        heatmap_resized = cv2.resize(heatmap_np, (img_resized.shape[1], img_resized.shape[0]))
        heatmap_uint8 = np.uint8(255 * heatmap_resized)
        heatmap_colored = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)

        next_step()  # 90% - Redimensionnement
        next_step()  # 95% - Superposition

        # Superposition des images
        img_bgr = cv2.cvtColor(img_resized, cv2.COLOR_RGB2BGR)
        superimposed_img = cv2.addWeighted(img_bgr, 0.6, heatmap_colored, 0.4, 0)

        next_step()  # 100% - Terminé

        return cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB)

    except Exception as e:
        import traceback
        traceback.print_exc()
        _update_progress(progress, 100, "❌ Erreur")
        return np.array(image_pil)

# ---- GESTION ASYNCHRONE / ÉTAT ----
current_image = None
current_predictions = None

# ---- Fonctions pour l'UI Gradio ----
def quick_predict_ui(image_pil):
    global current_image, current_predictions
    if image_pil is None:
        return "Veuillez uploader une image.", None, None, "❌ Erreur: Aucune image fournie."
    try:
        current_image = image_pil
        all_preds = predict_single(image_pil)
        current_predictions = all_preds
        ensemble_probs = all_preds["ensemble"]

        top_class_idx = int(np.argmax(ensemble_probs))
        top_class_name = CLASS_NAMES[top_class_idx]
        global_diag = diagnosis_map[top_class_name]

        for top in description:
            if top == top_class_name:
                desc_top = description[top]

        confidences = {CLASS_NAMES[i]: float(ensemble_probs[i] * 100) for i in range(len(CLASS_NAMES))}
        top_class_pourcent = round(max(confidences.values()), 2)

                # Ajoute une classe CSS en fonction du diagnostic
        if global_diag == "Malin":
            global_diag_html = f'<div style="float:left"><img src="https://huggingface.co/spaces/ericjedha/skin_care/resolve/main/mel.webp" width="150"></div><span style="font-size:20px">Diagnotic Global 💬</span><div class="highlight malin">{global_diag} : {top_class_pourcent} % ▪ {top_class_name.upper()} ▪</div>'        
        elif global_diag == "Bénin":
            global_diag_html = f'<div style="float:left"><img src="https://huggingface.co/spaces/ericjedha/skin_care/resolve/main/non-mel.webp" width="150"></div><span style="font-size:20px">Diagnotic Global 💬</span><div class="highlight benin">{global_diag} : {top_class_pourcent} % ▪ {top_class_name.upper()} ▪</div>'
        else:
            global_diag_html = global_diag  # Pas de surbrillance
        
        df = pd.DataFrame.from_dict(confidences, orient='index', columns=['Probabilité']).reset_index().rename(columns={'index': 'Classe'})
        df = df.sort_values(by='Probabilité', ascending=False)
        df['Pourcentage'] = df['Probabilité'].apply(lambda x: f"{x:.1f}%")

        fig = px.bar(df, 
                     x="Classe", 
                     y="Probabilité", 
                     color="Probabilité",
                     color_continuous_scale=px.colors.sequential.Viridis,
                     title="Probabilités par classe",
                     text="Pourcentage")

        text_positions = []
        for val in df['Probabilité']:
            if val <= 10:
                text_positions.append("outside")
            else:
                text_positions.append("inside")
        fig.update_traces(textposition=text_positions)
        fig.update_layout(xaxis_title="", yaxis_title="Probabilité (%)", height=400)



        return f"{global_diag_html}", desc_top, fig, "✅ Analyse terminée. Prêt pour Grad-CAM."
    except Exception as e:
        return f"Erreur: {e}", None, "❌ Erreur lors de l'analyse."

def generate_gradcam_ui(progress=gr.Progress()):
    global current_image, current_predictions
    if current_image is None or current_predictions is None:
        return None, "❌ Aucun résultat précédent — lance d'abord l'analyse rapide."
    
    try:
        ensemble_probs = current_predictions["ensemble"]
        top_class_idx = int(np.argmax(ensemble_probs))

        # Sélection des modèles disponibles
        candidates = []
        if model_xcept is not None:
            candidates.append(("xception", model_xcept, current_predictions["xception"][top_class_idx]))
        if model_resnet50 is not None:
            candidates.append(("resnet50", model_resnet50, current_predictions["resnet50"][top_class_idx]))
        if model_densenet is not None:
            candidates.append(("densenet201", model_densenet, current_predictions["densenet201"][top_class_idx]))

        if not candidates:
            return None, "❌ Aucun modèle disponible pour Grad-CAM."

        # Choix du meilleur modèle
        explainer_model_name, explainer_model, conf = max(candidates, key=lambda t: t[2])
        explainer_layer = LAST_CONV_LAYERS.get(explainer_model_name)

        # Génération Grad-CAM avec progression fluide
        gradcam_img = make_gradcam(
            current_image,
            explainer_model,
            explainer_layer,
            class_index=top_class_idx,
            progress=progress
        )

        return gradcam_img, f"✅ Grad-CAM généré avec {explainer_model_name} (confiance: {conf:.1%})"

    except Exception as e:
        import traceback
        traceback.print_exc()
        return None, f"❌ Erreur: {e}"

# ---- INTERFACE GRADIO ----
example_paths = ["ISIC_0024627.jpg", "ISIC_0025539.jpg", "ISIC_0031410.jpg"]

with gr.Blocks(theme=theme, title="Analyse de lésions", css=css) as demo:
    gr.Markdown("# 🔬 Skin Care : analyse de lésions cutanées")
    
    models_status = []
    if model_resnet50: models_status.append("✅ ResNet50")
    if model_densenet: models_status.append("✅ DenseNet201")
    if model_xcept: models_status.append("✅ Xception")
    gr.Markdown(f"**Avertissement 🚨:** cette application est un projet d'étudiant et ne doit être utilisé qu'à titre informatif. Seul votre médecin est habilité à vous donner un diagnostic.")
    gr.Markdown(f"**Modèles chargés:** {', '.join(models_status) if models_status else 'AUCUN'}")
    
    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(type="pil", label="📸 Uploader une image")
            with gr.Row():
                quick_btn = gr.Button("Étape 1️⃣ Analyse Rapide", variant="primary")
                gradcam_btn = gr.Button("Étape 2️⃣ Carte de chaleur", variant="secondary")
            gr.Examples(examples=example_paths, inputs=input_image)
        with gr.Column(scale=2):
            output_label = gr.HTML(value='<h2 class="output-class">Veuillez uploader une image.</h2>', elem_classes="diagnostic-global")
            output_text = gr.Label(label=" Explication", elem_classes="feedback")
            output_plot = gr.Plot(label="📈 Probabilités") 
            output_gradcam = gr.Image(label="🔍 Visualisation Grad-CAM")
            output_status = gr.Textbox(label="Statut", interactive=False)

    quick_btn.click(fn=quick_predict_ui, inputs=input_image, outputs=[output_label, output_text, output_plot, output_status])
    gradcam_btn.click(fn=generate_gradcam_ui, inputs=[], outputs=[output_gradcam, output_status])

if __name__ == "__main__":
    if all(m is None for m in [model_resnet50, model_densenet, model_xcept]):
        print("\n\n🚨 ATTENTION: Aucun modèle n'a été chargé. L'application ne fonctionnera pas.\n\n")
    demo.launch()