seperating mri and xray

app.py

@@ -3,49 +3,118 @@ import numpy as np
 import tensorflow as tf
 from PIL import Image
 
-# 1. Load the Keras model directly from the local folder
-print("Loading model...")
-model = tf.keras.models.load_model("mri.keras")
+# ==========================================
+# 1. MRI Model Setup (Your Existing Model)
+# ==========================================
+print("Loading MRI model...")
+mri_model = tf.keras.models.load_model("mri.keras")
+mri_class_names = ['Glioma', 'Meningioma', 'No Tumor', 'Pituitary Tumor']
 
-# 2. Class mappings based on the notebook training
-# Order: 'Glioma', 'Meningioma', 'Notumor', 'Pituitary'
-class_names = ['Glioma', 'Meningioma', 'No Tumor', 'Pituitary Tumor']
-
-def predict(image):
+def predict_mri(image):
     if image is None:
         return None
     
-    # 3. Preprocess the input
-    # Expected: 168x168, grayscale, scaled by 1/255.0, with batch dimension
-    
-    # Convert image to grayscale and resize it to 168x168
+    # Preprocess the MRI
     img = Image.fromarray(image).convert('L') 
     img = img.resize((168, 168))
     
-    # Convert to numpy array and normalize pixel values to [0, 1]
     img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=-1)
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Predict
+    predictions = mri_model.predict(img_array)[0]
+    confidences = {mri_class_names[i]: float(predictions[i]) for i in range(len(mri_class_names))}
+    return confidences
+
+
+# ==========================================
+# 2. X-Ray Model Setup (Reconstructing from Weights)
+# ==========================================
+print("Building X-Ray model architecture...")
+
+# The 14 classes from the NIH Chest X-Ray dataset
+xray_class_names = [
+    'Cardiomegaly', 'Emphysema', 'Effusion', 'Hernia', 'Infiltration', 
+    'Mass', 'Nodule', 'Atelectasis', 'Pneumothorax', 'Pleural_Thickening', 
+    'Pneumonia', 'Fibrosis', 'Edema', 'Consolidation'
+]
+
+def build_xray_model():
+    # Based on the Kaggle dataset description, the weights were trained 
+    # on an EfficientNetB1 with a 128x128 input size.
+    base_model = tf.keras.applications.EfficientNetB1(
+        input_shape=(128, 128, 3), 
+        weights=None, # We are loading custom weights next
+        include_top=False
+    )
+    
+    model = tf.keras.Sequential([
+        base_model,
+        tf.keras.layers.GlobalAveragePooling2D(),
+        tf.keras.layers.Dense(1024, activation='relu'),
+        tf.keras.layers.Dense(len(xray_class_names), activation='sigmoid') # Sigmoid for multi-label
+    ])
     
-    # The model expects input shape: (batch_size, 168, 168, 1)
-    img_array = np.expand_dims(img_array, axis=-1) # Add channel dimension
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    # Load the downloaded weights
+    model.load_weights("xray.h5")
+    return model
+
+xray_model = build_xray_model()
+print("X-Ray model loaded successfully.")
+
+def predict_xray(image):
+    if image is None:
+        return None
+    
+    # Preprocess the X-Ray input
+    img = Image.fromarray(image).convert('RGB') # EfficientNet expects 3 channels
+    img = img.resize((128, 128)) # The Kaggle dataset used 128x128
     
-    # 4. Make prediction
-    predictions = model.predict(img_array)[0]
+    img_array = np.array(img)
+    # Keras EfficientNet applications have built-in rescaling, 
+    # so we skip the / 255.0 step here.
     
-    # 5. Map the output to a clean, human-readable format for Gradio Interface
-    # Convert probabilities to a dictionary mapping class name to confidence score
-    confidences = {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
+    img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
+    
+    # Predict
+    predictions = xray_model.predict(img_array)[0]
+    
+    # Map probabilities to class names
+    confidences = {xray_class_names[i]: float(predictions[i]) for i in range(len(xray_class_names))}
     return confidences
 
-# 6. Define the Gradio interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(label="Upload MRI Brain Scan"),
-    outputs=gr.Label(num_top_classes=4, label="Prediction Confidence"),
-    title="MRI Brain Tumor Classification",
-    description="Upload an MRI scan to classify it into one of four categories: Glioma, Meningioma, No Tumor, or Pituitary Tumor.",
-    flagging_mode="never"
-)
+
+# ==========================================
+# 3. Define the Gradio Interface with Tabs
+# ==========================================
+with gr.Blocks(title="Medical Scan Classification") as interface:
+    gr.Markdown("# 🩺 Medical Scan Classifier")
+    gr.Markdown("Upload an **MRI Brain Scan** or a **Chest X-Ray** into the respective tabs below for AI-powered classification.")
+    
+    with gr.Tabs():
+        # --- TAB 1: MRI ---
+        with gr.TabItem("MRI Brain Scan"):
+            with gr.Row():
+                with gr.Column():
+                    mri_input = gr.Image(label="Upload MRI Brain Scan")
+                    mri_button = gr.Button("Classify MRI", variant="primary")
+                with gr.Column():
+                    mri_output = gr.Label(num_top_classes=4, label="Prediction Confidence")
+            
+            mri_button.click(fn=predict_mri, inputs=mri_input, outputs=mri_output)
+            
+        # --- TAB 2: X-Ray ---
+        with gr.TabItem("Chest X-Ray"):
+            with gr.Row():
+                with gr.Column():
+                    xray_input = gr.Image(label="Upload Chest X-Ray")
+                    xray_button = gr.Button("Classify X-Ray", variant="primary")
+                with gr.Column():
+                    # Displaying top 5 conditions since there are 14 possible labels
+                    xray_output = gr.Label(num_top_classes=5, label="Top 5 Predicted Conditions")
+                    
+            xray_button.click(fn=predict_xray, inputs=xray_input, outputs=xray_output)
 
 # Launch the app
 if __name__ == "__main__":