add gradio app and model

app.py

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+import gradio as gr
+import torch
+from transformers import CvtForImageClassification, AutoFeatureExtractor
+from PIL import Image
+import os
+
+# Configuración del dispositivo
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Cargar el extractor de características de Hugging Face
+extractor = AutoFeatureExtractor.from_pretrained("microsoft/cvt-13")
+
+# Definir las clases en el mismo orden que el modelo las predice
+class_names = [
+    "glioma_tumor",
+    "meningioma_tumor",
+    "no_tumor",
+    "pituitary_tumor"
+]
+
+# Función para cargar el modelo (solo una vez)
+def load_model():
+    model_dir = "models"  # Ruta a los pesos
+    model_file_pytorch = "cvt_model.pth"
+    
+    # Cargar los pesos del modelo desde el archivo .pth
+    checkpoint = torch.load(os.path.join(model_dir, model_file_pytorch), map_location=device)
+    
+    # Cargar el modelo dependiendo de si tenemos el modelo completo o solo los pesos
+    if isinstance(checkpoint, CvtForImageClassification):
+        model_pytorch = checkpoint  # El checkpoint ya es un modelo completo
+    else:
+        model_pytorch = CvtForImageClassification.from_pretrained("microsoft/cvt-13")
+        model_pytorch.load_state_dict(checkpoint)  # Cargar los pesos en el modelo
+    
+    model_pytorch.to(device)
+    model_pytorch.eval()
+    return model_pytorch
+
+# Cargar el modelo una vez cuando la app se inicie
+model_pytorch = load_model()
+
+# Función para hacer predicción con la imagen cargada
+def predict_image(image):
+    # Preprocesar la imagen usando el extractor de características
+    inputs = extractor(images=image, return_tensors="pt").to(device)
+    
+    # Hacer la predicción con el modelo
+    with torch.no_grad():
+        outputs = model_pytorch(**inputs)
+    
+    # Obtener los logits de la salida
+    logits = outputs.logits
+    
+    # Convertir los logits en probabilidades
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    
+    # Obtener la clase predicha (índice con mayor probabilidad)
+    predicted_index = probabilities.argmax(dim=-1).item()
+
+    # Mapear el índice de la clase predicha al nombre de la clase
+    predicted_class = class_names[predicted_index]
+
+    # Retornar el nombre de la clase predicha
+    return predicted_class
+# Función para limpiar los inputs
+def clear_inputs():
+    return None, None, None
+
+# Definir el tema y la interfaz de Gradio
+theme = gr.themes.Soft(
+    primary_hue="indigo",
+    secondary_hue="indigo",
+).set(
+    background_fill_primary='#121212',  # Dark background
+    background_fill_secondary='#1e1e1e',
+    block_background_fill='#1e1e1e',  # Almost black
+    block_border_color='#333',
+    block_label_text_color='#fffff',
+    block_label_text_color_dark = '#fffff',
+    block_title_text_color_dark = '#fffff',
+    button_primary_background_fill='#4f46e5',  # Violet
+    button_primary_background_fill_hover='#2563eb',  # Light blue
+    button_secondary_background_fill='#4f46e5',
+    button_secondary_background_fill_hover='#2563eb',
+    input_background_fill='#333',  # Dark grey
+    input_border_color='#444',  # Intermediate grey
+    block_label_background_fill='#4f46e5',
+    block_label_background_fill_dark='#4f46e5',
+    slider_color='#2563eb',
+    slider_color_dark='#2563eb',
+    button_primary_text_color='#fffff',
+    button_secondary_text_color='#fffff',
+    button_secondary_background_fill_hover_dark='#4f46e5',
+    button_cancel_background_fill_hover='#444',
+    button_cancel_background_fill_hover_dark='#444'
+)
+
+with gr.Blocks(theme=theme, css="""
+    body, gradio-app {
+      background-image: url('https://b2928487.smushcdn.com/2928487/wp-content/uploads/2022/04/Brain-inspiredAI-2048x1365.jpeg?lossy=1&strip=1&webp=1'); 
+      background-size: cover;
+      color: white;
+    }
+    .gradio-container {
+      background-color: transparent;
+      background-image: url('https://b2928487.smushcdn.com/2928487/wp-content/uploads/2022/04/Brain-inspiredAI-2048x1365.jpeg?lossy=1&strip=1&webp=1') !important; 
+      background-size: cover !important;
+      color: white;
+    }
+    .gradio-container .gr-dropdown-container select::after {
+        content: '▼'; 
+        color: white; 
+        padding-left: 5px; 
+    }
+    .gradio-container .gr-dropdown-container select:focus {
+        outline: none; 
+        border-color: #4f46e5;
+    }
+    .gradio-container select {
+      color: white;
+    }  
+    input, select, span, button, svg, .secondary-wrap {
+      color: white; 
+    }
+               
+    h1 {
+        color: white;
+        font-size: 4em;  
+        margin: 20px auto;
+    }
+    .gradio-container h1 { 
+        font-size: 5em;  
+        color: white; 
+        text-align: center; 
+        text-shadow: 2px 2px 0px #8A2BE2,  
+                     4px 4px 0px #00000033; 
+        text-transform: uppercase;
+        margin: 18px auto;
+    }
+    .gradio-container input { 
+        color: white; 
+    }
+    .gradio-container .output { 
+        color: white; 
+    }
+    .required-dropdown li {
+      color: white;
+    }
+    .button-style {
+      background-color: #4f46e5;
+      color: white;
+    }
+    .button-style:hover {
+      background-color: #2563eb;
+      color: white;
+    }
+               
+    .gradio-container .contain textarea {
+      color: white;
+      font-weight: 600;
+      font-size: 1.5rem;
+    }
+    .contain textarea {
+      color: white;
+      font-weight: 600;
+      font-size: 1.5rem;
+    }     
+    textarea {
+      color: white;
+      font-weight: 600;
+      font-size: 1.5rem;
+      background-color: black;
+    }     
+    textarea .scroll-hide {
+      color: white;
+    }
+    .scroll-hide svelte-1f354aw {
+      color: white;
+    }
+    """) as demo:
+
+    gr.Markdown("# Brain Tumor Classification 🧠")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Sube la imagen")
+            model_input = gr.Dropdown(choices=["model_1", "model_2"], label="Selecciona un modelo", elem_classes=['required-dropdown'])
+            classify_btn = gr.Button("Clasificar", elem_classes=['button-style'])
+            clear_btn = gr.Button("Limpiar")
+        with gr.Column():
+            prediction_output = gr.Textbox(label="Predicción")
+    
+    classify_btn.click(predict_image, inputs=[image_input], outputs=prediction_output)
+    clear_btn.click(clear_inputs, inputs=[], outputs=[image_input, model_input, prediction_output])
+
+demo.launch()

models/cvt_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e10dc9a09c803275139d55eeec5cd0414507661eab754e89991f5c1fc6c9bcbf
+size 80316819