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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+img3.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,269 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import json
+import requests
+import io
+import base64
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU
+
+# ===== CONFIGURACIÓN =====
+PLANTNET_CONFIG = {
+    "base_url": "https://my-api.plantnet.org/v2/identify/all",
+    "api_key": "2b10GtUDt6p1whX94wlEiR3CG",
+    "timeout": 10,
+    "lang": "es"  # Español
+}
+
+# ===== VARIABLES GLOBALES =====
+MODEL_LOADED = False
+model = None
+labels = []
+
+# ===== FUNCIONES DE UTILIDAD =====
+def load_model():
+    global model, labels, MODEL_LOADED
+    try:
+        model = tf.keras.models.load_model("flores_modelo (2).h5")
+        with open("clases_orden_oxford.json", "r") as f:
+            class_indices = json.load(f)
+        labels = [None] * len(class_indices)
+        for class_name, idx in class_indices.items():
+            labels[idx] = class_name.replace("_", " ").title()
+        MODEL_LOADED = True
+    except Exception as e:
+        print(f"Error al cargar el modelo: {e}")
+        MODEL_LOADED = False
+
+def image_to_base64(image_path):
+    try:
+        with open(image_path, "rb") as img_file:
+            return base64.b64encode(img_file.read()).decode('utf-8')
+    except:
+        return None
+
+def preprocess_image(image):
+    image = image.resize((224, 224))
+    img_array = np.array(image) / 255.0
+    if img_array.shape[-1] == 4:
+        img_array = img_array[..., :3]
+    return np.expand_dims(img_array, axis=0)
+
+# =====സ
+
+# ===== INTEGRACIÓN CON PLANTNET =====
+def get_flower_info_from_plantnet(flower_name, image_array):
+    try:
+        image_pil = Image.fromarray((image_array[0] * 255).astype(np.uint8))
+        img_byte_arr = io.BytesIO()
+        image_pil.save(img_byte_arr, format='JPEG')
+        img_byte_arr.seek(0)
+
+        url = f"{PLANTNET_CONFIG['base_url']}?api-key={PLANTNET_CONFIG['api_key']}&lang={PLANTNET_CONFIG['lang']}"
+        files = {'images': ('image.jpg', img_byte_arr, 'image/jpeg')}
+        data = {'organs': 'flower'}
+
+        response = requests.post(url, files=files, data=data, timeout=PLANTNET_CONFIG['timeout'])
+        if response.status_code == 200:
+            return parse_plantnet_response(response.json(), flower_name)
+        else:
+            print(f"Error en PlantNet: {response.status_code}")
+            return get_fallback_info(flower_name)
+    except requests.exceptions.Timeout:
+        print("Tiempo de espera agotado en PlantNet")
+        return get_fallback_info(flower_name)
+    except Exception as e:
+        print(f"PlantNet error: {e}")
+        return get_fallback_info(flower_name)
+
+def parse_plantnet_response(data, flower_name):
+    if 'results' in data and len(data['results']) > 0:
+        results = data['results'][:3]
+        info = f"""
+<div class="bg-[rgba(255,255,255,0.1)] border-2 border-[rgba(255,165,0,0.3)] rounded-2xl p-5 m-[15px_0] shadow-[0_8px_32px_rgba(0,0,0,0.2)]">
+    <div class="flex items-center justify-center">
+        <h2 class="text-yellow-400 font-bold text-2xl">🌸 {flower_name}</h2>
+    </div>
+    <div>
+        <h3 class="text-yellow-400 font-bold text-xl mt-4">📊 Identificación Científica</h3>
+"""
+        for i, result in enumerate(results, 1):
+            species = result.get('species', {})
+            scientific_name = species.get('scientificNameWithoutAuthor', 'N/A')
+            authorship = species.get('scientificNameAuthorship', '')
+            common_names = species.get('commonNames', [])
+            family = species.get('family', {}).get('scientificNameWithoutAuthor', 'N/A')
+            genus = species.get('genus', {}).get('scientificNameWithoutAuthor', 'N/A')
+            score = result.get('score', 0)
+            common_names_str = ', '.join(common_names[:3]) if common_names else 'No disponible'
+
+            info += f"""
+        <div class="flex items-start mt-4">
+            <div class="text-white mr-4">#{i}</div>
+            <div>
+                <h4 class="text-white font-semibold">{scientific_name} {authorship}</h4>
+                <div class="relative h-2 bg-gray-200 rounded mt-2">
+                    <div class="absolute h-full bg-yellow-400 rounded" style="width: {score*100}%"></div>
+                    <span class="text-white text-sm mt-2 block">{score:.1%} confianza</span>
+                </div>
+                <div class="grid grid-cols-1 gap-2 mt-2">
+                    <div class="text-white"><strong>Nombres comunes:</strong> {common_names_str}</div>
+                    <div class="text-white"><strong>Familia:</strong> {family}</div>
+                    <div class="text-white"><strong>Género:</strong> {genus}</div>
+                </div>
+            </div>
+        </div>
+"""
+        info += """
+    </div>
+    <div class="mt-6">
+        <h3 class="text-yellow-400 font-bold text-xl">🌿 Cuidados Generales</h3>
+        <div class="grid grid-cols-2 gap-4 mt-2">
+            <div class="flex items-start"><div class="text-2xl mr-2">☀️</div><div class="text-white"><strong>Luz:</strong> Luz solar directa o indirecta</div></div>
+            <div class="flex items-start"><div class="text-2xl mr-2">💧</div><div class="text-white"><strong>Riego:</strong> Mantener húmedo, evitar exceso</div></div>
+            <div class="flex items-start"><div class="text-2xl mr-2">🌡️</div><div class="text-white"><strong>Temperatura:</strong> Evitar cambios bruscos</div></div>
+            <div class="flex items-start"><div class="text-2xl mr-2">🌱</div><div class="text-white"><strong>Suelo:</strong> Bien drenado y rico en nutrientes</div></div>
+        </div>
+    </div>
+</div>
+"""
+        return info
+    return get_fallback_info(flower_name)
+
+def get_fallback_info(flower_name):
+    return f"""
+<div class="bg-[rgba(255,255,255,0.1)] border-2 border-[rgba(255,165,0,0.3)] rounded-2xl p-5 m-[15px_0] shadow-[0_8px_32px_rgba(0,0,0,0.2)]">
+    <div class="flex items-center justify-between">
+        <h2 class="text-yellow-400 font-bold text-2xl">🌸 {flower_name}</h2>
+        <div class="bg-red-600 text-white px-2 py-1 rounded">PlantNet no disponible</div>
+    </div>
+    <div class="mt-4">
+        <h3 class="text-yellow-400 font-bold text-xl">📖 Información General</h3>
+        <p class="text-white">Identificado por nuestro modelo de IA entrenado en el conjunto de datos Oxford 102 Flowers.</p>
+        <div class="grid grid-cols-2 gap-4 mt-4">
+            <div class="text-white">
+                <h4 class="font-semibold">🌺 Características</h4>
+                <ul class="list-disc ml-5">
+                    <li>Estructuras reproductivas de la planta</li>
+                    <li>Varios colores y formas</li>
+                    <li>Evolucionadas para atraer polinizadores</li>
+                </ul>
+            </div>
+            <div class="text-white">
+                <h4 class="font-semibold">🎯 Cuidados Básicos</h4>
+                <ul class="list-disc ml-5">
+                    <li>Buena iluminación según la especie</li>
+                    <li>Riego regular sin exceso</li>
+                    <li>Temperatura estable</li>
+                    <li>Fertilización adecuada</li>
+                </ul>
+            </div>
+        </div>
+    </div>
+</div>
+"""
+
+# ===== LÓGICA DE PREDICCIÓN =====
+def predict(image):
+    if not image:
+        return "No se cargó ninguna imagen", "0%", """
+<div class="bg-red-600 text-white rounded-2xl p-5 m-[15px_0]">
+    <h3 class="text-xl font-bold">⚠️ Imagen requerida</h3>
+    <p>Por favor, carga una imagen de una flor para iniciar la identificación.</p>
+</div>
+"""
+    if not MODEL_LOADED:
+        return "Error en el modelo", "0%", """
+<div class="bg-red-600 text-white rounded-2xl p-5 m-[15px_0]">
+    <h3 class="text-xl font-bold">🚫 Modelo no disponible</h3>
+    <p>No se pudo cargar el modelo de clasificación. Verifica los archivos del modelo.</p>
+</div>
+"""
+    try:
+        img_array = preprocess_image(image)
+        preds = model.predict(img_array)
+        class_idx = np.argmax(preds[0])
+        confidence = preds[0][class_idx]
+        label_name = labels[class_idx]
+        flower_details = get_flower_info_from_plantnet(label_name, img_array)
+        return label_name, f"{confidence:.2%}", flower_details
+    except Exception as e:
+        return "Error", "0%", f"""
+<div class="bg-red-600 text-white rounded-2xl p-5 m-[15px_0]">
+    <h3 class="text-xl font-bold">❌ Error de predicción</h3>
+    <p>Error durante el procesamiento: {str(e)}</p>
+</div>
+"""
+
+# ===== CSS PARA EL FONDO DE LA APLICACIÓN =====
+custom_css = """
+.gradio-container {
+    background: #FFFFFF; 
+    min-height: 100vh;
+}
+"""
+
+# ===== JAVASCRIPT PARA OPTIMIZACIÓN DE CÁMARA =====
+camera_js_improved = """
+<script>
+function initCameraOptimization() {
+    function ensureButtonsVisible() {
+        document.querySelectorAll('[data-testid="image"]').forEach(container => {
+            container.querySelectorAll('button').forEach(button => {
+                button.style.display = 'flex';
+                button.style.visibility = 'visible';
+                button.style.opacity = '1';
+                button.style.zIndex = '999';
+            });
+        });
+    }
+    ensureButtonsVisible();
+    setInterval(ensureButtonsVisible, 5000);
+}
+if (document.readyState === 'loading') {
+    document.addEventListener('DOMContentLoaded', initCameraOptimization);
+} else {
+    initCameraOptimization();
+}
+</script>
+"""
+
+# ===== APLICACIÓN PRINCIPAL =====
+load_model()
+img3_b64 = image_to_base64("img3.png")
+img2_b64 = image_to_base64("img2.png")
+
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="🌸 Flower ") as demo:
+    header_html = f'''
+    <div style="background: rgba(37, 58, 105); border-radius: 16px; padding: 16px; margin-bottom: 24px; border: 1px solid #e5e7eb; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); max-width: 1024px; margin-left: auto; margin-right: auto;">
+        <div style="display: flex; align-items: center; justify-content: space-between; gap: 16px;">
+            {'<div style="flex-shrink: 0; width: 150px; height: 150px;"><img src="data:image/png;base64,' + img3_b64 + '" alt="Logo" style="width: 100%; height: 100%; object-fit: contain; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);"></div>' if img3_b64 else '<div style="width: 80px; height: 80px;"></div>'}
+            <div style="text-align: center;">
+                <h1 style="color: #FFCC00; font-size: 24px; font-weight: bold; margin-bottom: 4px;">IDENTIFICADOR DE FLORES</h1>
+                <p style="color: #FFFFFF; font-weight: 500; font-size: 14px; margin-bottom: 4px;">Identifica cualquier flor en cuestión de segundos</p>
+                <p style="color: #22c55e; font-size: 12px;">Oxford 102 Flowers + PlantNet</p>
+            </div>
+            {'<div style="flex-shrink: 0; width: 150px; height: 150px;"><img src="data:image/png;base64,' + img2_b64 + '" alt="Logo" style="width: 100%; height: 100%; object-fit: contain; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);"></div>' if img2_b64 else '<div style="width: 48px; height: 48px;"></div>'}
+        </div>
+    </div>
+    '''
+    gr.HTML(header_html)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="📷 Cargar Imagen de Flor", height=350, sources=["upload", "webcam", "clipboard"])
+            predict_btn = gr.Button("🔍 Identificar Flor", variant="primary")
+            gr.HTML(f'<div class="bg-[rgba(255,255,255,0.1)] border-2 border-[rgba(255,165,0,0.3)] rounded-2xl p-5 m-[15px_0] shadow-[0_8px_32px_rgba(0,0,0,0.2)]"><h3 class="text-yellow-400 font-bold text-xl">📊 Estado del Sistema</h3><p class="text-white">Modelo: {"✅ Activo" if MODEL_LOADED else "❌ Error"}</p><p class="text-white">Clases: {len(labels)}</p></div>')
+
+        with gr.Column(scale=1):
+            result_label = gr.Textbox(label="🌼 Flor Identificada", interactive=False, placeholder="El nombre de la flor aparecerá aquí...")
+            result_conf = gr.Textbox(label="📊 Confianza", interactive=False, placeholder="El nivel de confianza aparecerá aquí...")
+            flower_info_output = gr.HTML(value='<div class="bg-[rgba(255,255,255,0.1)] border-2 border-[rgba(255,165,0,0.3)] rounded-2xl p-5 m-[15px_0] shadow-[0_8px_32px_rgba(0,0,0,0.2)]"><h3 class="text-yellow-400 font-bold text-xl">🌸 ¡Bienvenido!</h3><p class="text-white">Carga una imagen de una flor para iniciar la identificación.</p></div>')
+
+    predict_btn.click(fn=predict, inputs=image_input, outputs=[result_label, result_conf, flower_info_output])
+    gr.HTML(camera_js_improved)
+
+if __name__ == "__main__":
+    demo.launch(share=False)

clases_orden_oxford.json

@@ -0,0 +1,104 @@
+{
+  "pink_primrose": 0,
+  "hard_leaved_pocket_orchid": 1,
+  "canterbury_bells": 2,
+  "sweet_pea": 3,
+  "english_marigold": 4,
+  "tiger_lily": 5,
+  "moon_orchid": 6,
+  "bird_of_paradise": 7,
+  "monkshood": 8,
+  "globe_thistle": 9,
+  "snapdragon": 10,
+  "colt's_foot": 11,
+  "king_protea": 12,
+  "spear_thistle": 13,
+  "yellow_iris": 14,
+  "globe_flower": 15,
+  "purple_coneflower": 16,
+  "peruvian_lily": 17,
+  "balloon_flower": 18,
+  "giant_white_arum_lily": 19,
+  "fire_lily": 20,
+  "pincushion_flower": 21,
+  "fritillary": 22,
+  "red_ginger": 23,
+  "grape_hyacinth": 24,
+  "corn_poppy": 25,
+  "prince_of_wales_feathers": 26,
+  "stemless_gentian": 27,
+  "artichoke": 28,
+  "sweet_william": 29,
+  "carnation": 30,
+  "garden_phlox": 31,
+  "love_in_the_mist": 32,
+  "mexican_aster": 33,
+  "alpine_sea_holly": 34,
+  "ruby_lipped_cattleya": 35,
+  "cape_flower": 36,
+  "great_masterwort": 37,
+  "siam_tulip": 38,
+  "lenten_rose": 39,
+  "barberton_daisy": 40,
+  "daffodil": 41,
+  "sword_lily": 42,
+  "poinsettia": 43,
+  "bolero_deep_blue": 44,
+  "wallflower": 45,
+  "marigold": 46,
+  "buttercup": 47,
+  "oxeye_daisy": 48,
+  "common_dandelion": 49,
+  "petunia": 50,
+  "wild_pansy": 51,
+  "primula": 52,
+  "sunflower": 53,
+  "pelargonium": 54,
+  "bishop_of_llandaff": 55,
+  "gaura": 56,
+  "geranium": 57,
+  "orange_dahlia": 58,
+  "pink_yellow_dahlia": 59,
+  "cautleya_spicata": 60,
+  "japanese_anemone": 61,
+  "black_eyed_susan": 62,
+  "silverbush": 63,
+  "californian_poppy": 64,
+  "osteospermum": 65,
+  "spring_crocus": 66,
+  "bearded_iris": 67,
+  "windflower": 68,
+  "tree_poppy": 69,
+  "gazania": 70,
+  "azalea": 71,
+  "water_lily": 72,
+  "rose": 73,
+  "thorn_apple": 74,
+  "morning_glory": 75,
+  "passion_flower": 76,
+  "lotus": 77,
+  "toad_lily": 78,
+  "anthurium": 79,
+  "frangipani": 80,
+  "clematis": 81,
+  "hibiscus": 82,
+  "columbine": 83,
+  "desert_rose": 84,
+  "tree_mallow": 85,
+  "magnolia": 86,
+  "cyclamen": 87,
+  "watercress": 88,
+  "canna_lily": 89,
+  "hippeastrum": 90,
+  "bee_balm": 91,
+  "ball_moss": 92,
+  "foxglove": 93,
+  "bougainvillea": 94,
+  "camellia": 95,
+  "mallow": 96,
+  "mexican_petunia": 97,
+  "bromelia": 98,
+  "blanket_flower": 99,
+  "trumpet_creeper": 100,
+  "blackberry_lily": 101
+}

flores_modelo (2).h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab59a8c20162846def8e70234e6c01cfe8a99389b4b61d5b114b4286421a8e74
+size 11136704

package-lock.json

@@ -0,0 +1,6 @@
+{
+  "name": "demo-clasificador-flores",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {}
+}