Delete app.py

app.py

@@ -1,170 +0,0 @@
-import gradio as gr
-import numpy as np
-import cv2
-from joblib import load
-import os
-
-# --- Configuration & Model Loading ---
-# Model path is confirmed to be in the root directory.
-MODEL_PATH = "orb_bow_svm.joblib"
-
-# --- Model Constants (should match your training environment) ---
-IMG_SIZE = (200, 200)
-VOCAB_SIZE = 300
-orb = cv2.ORB_create(nfeatures=500)
-
-# Default classes
-DEFAULT_CLASSES = [
-    "Non Demented",
-    "Very mild Dementia",
-    "Mild Dementia",
-    "Moderate Dementia"
-]
-
-# --- CORRECTED & EXPANDED EXAMPLE FILES (MUST MATCH UPLOADED FILES IN ROOT) ---
-# Includes all 7 image files found in your root directory
-EXAMPLE_IMAGES = [
-    "mild_9.jpg",
-    "moderate_7.jpg",
-    "non_93.jpg",
-    "verymild_986.jpg",
-    "moderate_36.jpg", # Newly added file
-    "verymild_795.jpg", # Newly added file
-    "verymild_8.jpg" # Newly added file
-]
-# -----------------------------------------------------------------------------
-
-# Attempt to load the model components
-kmeans, scaler, svm = None, None, None
-classes = DEFAULT_CLASSES
-
-try:
-    print(f"Attempting to load model from: {MODEL_PATH}")
-    model_data = load(MODEL_PATH)
-    kmeans = model_data["kmeans"]
-    scaler = model_data["scaler"]
-    svm = model_data["svm"]
-    classes = model_data["classes"]
-    print("Model loaded successfully!")
-except FileNotFoundError:
-    print(f"ERROR: Model file '{MODEL_PATH}' not found.")
-except Exception as e:
-    print(f"ERROR: An unexpected error occurred during model loading: {e}.")
-
-# Define real/dummy functions based on successful load
-if svm is None:
-    # Dummy functions for failure state
-    def encode(descriptors, kmeans_model): return np.zeros(VOCAB_SIZE)
-    def gradio_predict(input_img):
-        return "⚠️ Model not loaded. Cannot perform prediction.", {cls: 0.0 for cls in DEFAULT_CLASSES}
-else:
-    # Real functions for success state
-    def encode(descriptors, kmeans_model):
-        if descriptors is None or len(descriptors) == 0:
-            return np.zeros(VOCAB_SIZE)
-        words = kmeans_model.predict(descriptors)
-        hist, _ = np.histogram(words, bins=np.arange(VOCAB_SIZE + 1))
-        return hist
-    
-    def gradio_predict(input_img):
-        # Preprocessing
-        img = cv2.cvtColor(input_img, cv2.COLOR_RGB2GRAY)
-        img = cv2.resize(img, IMG_SIZE)
-
-        # Feature Extraction
-        kps, des = orb.detectAndCompute(img, None)
-        feat = encode(des, kmeans).reshape(1, -1)
-        feat_scaled = scaler.transform(feat)
-
-        # Prediction
-        prediction_index = svm.predict(feat_scaled)[0]
-        probabilities = svm.predict_proba(feat_scaled)[0]
-        
-        # Format output
-        predicted_class = classes[prediction_index]
-        confidence_score = probabilities[prediction_index] * 100
-        
-        output_message = f"**Diagnosis: {predicted_class}**\n" \
-                         f"Confidence: {confidence_score:.2f}%"
-        
-        prob_dict = {cls: prob for cls, prob in zip(classes, probabilities)}
-        
-        return output_message, prob_dict
-
-# --- Gradio Interface Definition (Unchanged layout/design) ---
-
-colorful_theme = gr.Theme.from_hub("gradio/seafoam")
-
-with gr.Blocks(theme=colorful_theme, title="Dementia Stage Classifier 🧠") as demo:
-    
-    gr.Markdown(
-        """
-        # 🧠 Brain Scan Analyzer: Dementia Stage Classification 🌟
-        ... (Your impressive introductory text) ...
-        """
-    )
-    
-    with gr.Row(variant="panel"):
-        # Input Column
-        with gr.Column(scale=1):
-            gr.Markdown("## 📤 Input Image")
-            image_input = gr.Image(
-                type="numpy", 
-                label="Upload MRI Brain Scan Image", 
-                height=350,
-                width=350,
-                interactive=True
-            )
-            
-            # Action Button
-            submit_btn = gr.Button("✨ Classify Scan ✨", variant="primary", size="lg")
-            gr.Markdown("---")
-            gr.Markdown(
-                """
-                ### 💡 Quick Test Examples:
-                Click on any image below to load and classify it instantly!
-                """
-            )
-            
-            # This section now always works because the file paths are confirmed.
-            gr.Examples(
-                examples=EXAMPLE_IMAGES,
-                inputs=image_input,
-                outputs=[gr.Textbox(), gr.Label()],
-                fn=gradio_predict,
-                cache_examples=True,
-            )
-
-
-        # Output Column
-        with gr.Column(scale=2):
-            gr.Markdown("## ✅ Prediction Results")
-            output_text = gr.Textbox(
-                label="Predicted Dementia Stage & Confidence", 
-                value="Upload an image and click 'Classify Scan' to see the results.",
-                lines=3,
-                show_copy_button=True,
-                elem_id="prediction_output_box" 
-            )
-            
-            output_label = gr.Label(
-                label="Detailed Probability Distribution",
-            )
-            
-            gr.Markdown(
-                """
-                ---
-                ### 📚 Our Methodology:
-                ... (Your methodology text) ...
-                """
-            )
-    
-    # Connect the button to the prediction function
-    submit_btn.click(
-        fn=gradio_predict,
-        inputs=[image_input],
-        outputs=[output_text, output_label]
-    )
-
-# Launch the Gradio app 
-demo.launch(share=True)