Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from joblib import load
+import os
+
+# --- Configuration & Model Loading ---
+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"
+]
+
+# --- FINAL CORRECTED & EXPANDED EXAMPLE FILES (MUST MATCH UPLOADED FILES IN ROOT) ---
+EXAMPLE_IMAGES = [
+    "mild_9.jpg",
+    "moderate_7.jpg",
+    "non_93.jpg",
+    "verymild_986.jpg",
+    "moderate_36.jpg", 
+    "verymild_795.jpg", 
+    "verymild_8.jpg" 
+]
+# -----------------------------------------------------------------------------
+
+# 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:
+    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:
+    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 ---
+
+colorful_theme = gr.Theme.from_hub("gradio/seafoam")
+
+with gr.Blocks(theme=colorful_theme, title="DementiaScan-Predict 🧠") as demo:
+    
+    # ------------------------------------------------
+    # INTRODUCTORY TEXT (UPDATED)
+    # ------------------------------------------------
+    gr.Markdown(
+        """
+        # 🧠 DementiaScan-Predict: Rapid Stage Classification 🌟
+        
+        Welcome! This tool offers **rapid, preliminary classification of dementia stages** from MRI brain scans. Our core innovation is providing highly **efficient and accessible AI diagnostics**, perfect for deployment in resource-constrained environments.
+        
+        ---
+        ### 🚀 How It Works:
+        1.  **Upload an MRI Scan** (T1-weighted image).
+        2.  **Click 'Classify Scan'** to trigger the analysis.
+        3.  **Get Instant Results** for the predicted dementia stage and confidence.
+        
+        ---
+        """
+    )
+    
+    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!
+                """
+            )
+            
+            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",
+            )
+            
+            # ------------------------------------------------
+            # METHODOLOGY TEXT (UPDATED)
+            # ------------------------------------------------
+            gr.Markdown(
+                """
+                ---
+                ### 📚 Methodology: ORB-BoVW-SVM
+                We employ a fast, classical Computer Vision pipeline for efficiency:
+                * **Feature Detection:** **ORB** detects key visual points on the brain scan.
+                * **Feature Encoding:** **Bag of Visual Words (BoVW)** converts these features into a compact, fixed-size histogram (vector).
+                * **Classification:** The resulting vector is classified using a **Support Vector Machine (SVM)**.
+                
+                This approach ensures excellent **speed and low computational overhead** compared to standard deep learning models.
+                """
+            )
+    
+    # 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)