Delete app.py

app.py

@@ -1,98 +0,0 @@
-import os
-import urllib.request
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.applications import EfficientNetB0
-from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization
-from tensorflow.keras.models import Model
-from tensorflow.keras.applications.efficientnet import preprocess_input
-import gradio as gr
-import cv2
-import spaces
-
-MODEL_PATH = "efficientnetb0_finetuned_brain_mri.keras"
-MODEL_URL = "https://huggingface.co/starpreeda/BrainTumorTest/resolve/main/efficientnetb0_finetuned_brain_mri.keras"
-
-def load_brain_mri_model():
-    if not os.path.exists(MODEL_PATH):
-        print("Downloading model weights from Hugging Face Repository...")
-        try:
-            urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
-            print("Model downloaded successfully!")
-        except Exception as e:
-            raise RuntimeError(f"ไม่สามารถดาวน์โหลดโมเดลได้: {e}") from e
-
-
-    base_model = EfficientNetB0(weights=None, include_top=False, input_shape=(224, 224, 3))
-    x = base_model.output
-    x = GlobalAveragePooling2D()(x)
-    x = BatchNormalization()(x)
-    x = Dense(256, activation='relu')(x)
-    x = Dropout(0.4)(x)
-    outputs = Dense(4, activation='softmax')(x)
-    
-    model = Model(inputs=base_model.input, outputs=outputs)
-    
-
-    model.load_weights(MODEL_PATH)
-    return model
-
-model = load_brain_mri_model()
-
-CLASS_MAPPING = {
-    'glioma': {'name': 'Glioma Tumor', 'desc': 'A type of tumor that originates in the glial cells.'},
-    'meningioma': {'name': 'Meningioma Tumor', 'desc': 'A tumor arising from the meninges.'},
-    'notumor': {'name': 'No Tumor Detected', 'desc': 'No clear evidence of brain tumor tissue.'},
-    'pituitary': {'name': 'Pituitary Tumor', 'desc': 'An abnormal growth located in the pituitary gland.'}
-}
-CLASS_NAMES = ['glioma', 'meningioma', 'notumor', 'pituitary']
-@spaces.GPU
-def predict_mri(input_img):
-    if input_img is None:
-        return "<h3 style='color:#d93025;'>Please upload a valid Brain MRI scan image.</h3>", {}
-
-    if input_img.ndim == 2:
-        input_img = cv2.cvtColor(input_img, cv2.COLOR_GRAY2RGB)
-    elif input_img.shape[-1] == 4:
-        input_img = cv2.cvtColor(input_img, cv2.COLOR_RGBA2RGB)
-
-    img_resized = cv2.resize(input_img, (224, 224))
-    img_array = img_resized.astype(np.float32)
-    img_batch = np.expand_dims(img_array, axis=0)
-    img_preprocessed = preprocess_input(img_batch)
-
-    predictions = model.predict(img_preprocessed)[0]
-
-    confidences = {}
-    for idx, class_key in enumerate(CLASS_NAMES):
-        confidences[CLASS_MAPPING[class_key]['name']] = float(predictions[idx])
-
-    top_idx = int(np.argmax(predictions))
-    top_key = CLASS_NAMES[top_idx]
-    top_confidence = predictions[top_idx] * 100
-    info = CLASS_MAPPING[top_key]
-
-    summary_html = f"""
-    <div style="background-color: #f8f9fa; border-left: 6px solid #1a73e8; padding: 18px; border-radius: 8px;">
-        <h3 style="color: #1a73e8; margin-top: 0;">Diagnostic Classification Summary</h3>
-        <p style="font-size: 20px; font-weight: bold;">Predicted Class: <span style="color: #d93025;">{info['name']}</span></p>
-        <p style="font-size: 16px; font-weight: bold;">Confidence Score: <span style="color: #188038;">{top_confidence:.2f}%</span></p>
-        <hr style="border: 0.5px solid #dadce0;">
-        <p style="font-size: 14px; color: #5f6368;"><b>Clinical Note:</b> {info['desc']}</p>
-    </div>
-    """
-    return summary_html, confidences
-
-demo = gr.Interface(
-    fn=predict_mri,
-    inputs=gr.Image(type="numpy", label="Upload Brain MRI Image"),
-    outputs=[
-        gr.HTML(label="Classification Result"),
-        gr.Label(num_top_classes=4, label="Class Probability Distribution")
-    ],
-    title="🧠 Brain Tumor MRI Classification System",
-    description="Upload a Brain MRI scan to analyze potential tumor types."
-)
-
-if __name__ == "__main__":
-    demo.queue().launch(server_name="0.0.0.0", server_port=7860)