Create app.py

app.py

@@ -0,0 +1,114 @@
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import tensorflow_hub as hub
+from PIL import Image
+
+# Load models
+model_initial = keras.models.load_model(
+    "models/initial_model.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+)
+model_tumor = keras.models.load_model(
+    "models/model_tumor.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+)
+model_stroke = keras.models.load_model(
+    "models/model_stroke.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+)
+model_alzheimer = keras.models.load_model(
+    "models/model_alzheimer.h5", custom_objects={'KerasLayer': hub.KerasLayer}
+)
+
+class CombinedDiseaseModel(tf.keras.Model):
+    def __init__(self, model_initial, model_alzheimer, model_tumor, model_stroke):
+        super(CombinedDiseaseModel, self).__init__()
+        self.model_initial = model_initial
+        self.model_alzheimer = model_alzheimer
+        self.model_tumor = model_tumor
+        self.model_stroke = model_stroke
+        self.disease_labels = ["Alzheimer's", 'No Disease', 'Stroke', 'Tumor']
+
+        self.sub_models = {
+            "Alzheimer's": model_alzheimer,
+            'Tumor': model_tumor,
+            'Stroke': model_stroke
+        }
+
+    def call(self, inputs):
+        initial_probs = self.model_initial(inputs, training=False)
+        main_disease_idx = tf.argmax(initial_probs, axis=1)
+        main_disease = self.disease_labels[main_disease_idx[0].numpy()]
+        main_disease_prob = initial_probs[0, main_disease_idx[0]].numpy()
+
+        if main_disease == 'No Disease':
+            sub_category = "No Disease"
+            sub_category_prob = main_disease_prob
+        else:
+            sub_model = self.sub_models[main_disease]
+            sub_category_pred = sub_model(inputs, training=False)
+            sub_category = tf.argmax(sub_category_pred, axis=1).numpy()[0]
+            sub_category_prob = sub_category_pred[0, sub_category].numpy()
+
+            if main_disease == "Alzheimer's":
+                sub_category_label = ['Very Mild', 'Mild', 'Moderate']
+            elif main_disease == 'Tumor':
+                sub_category_label = ['Glioma', 'Meningioma', 'Pituitary']
+            elif main_disease == 'Stroke':
+                sub_category_label = ['Ischemic', 'Hemorrhagic']
+
+            sub_category = sub_category_label[sub_category]
+
+        return f"The MRI image shows {main_disease} with a probability of {main_disease_prob*100:.2f}%.\nThe subcategory of {main_disease} is {sub_category} with a probability of {sub_category_prob*100:.2f}%."
+
+
+# Initialize the combined model
+cnn_model = CombinedDiseaseModel(
+    model_initial=model_initial,
+    model_alzheimer=model_alzheimer,
+    model_tumor=model_tumor,
+    model_stroke=model_stroke
+)
+
+
+def process_image(image):
+    image = image.resize((256, 256))
+    image.convert("RGB")
+    image_array = np.array(image) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+    predictions = cnn_model(image_array)
+    return predictions
+
+
+def gradio_interface(patient_info, query_type, image):
+    if image is not None:
+        image_response = process_image(image)
+        response = f"Patient Info: {patient_info}\nQuery Type: {query_type}\n{image_response}"
+        return response
+    else:
+        return "Please upload an image."
+
+
+# Create Gradio app
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(
+            label="Patient Information",
+            placeholder="Enter patient details here...",
+            lines=5,
+            max_lines=10
+        ),
+        gr.Textbox(
+            label="Query Type"
+        ),
+        gr.Image(
+            type="pil",
+            label="Upload an Image",
+        )
+    ],
+    outputs=gr.Textbox(label="Response", placeholder="The response will appear here..."),
+    title="Medical Diagnosis with MRI",
+    description="Upload MRI images and provide patient information for diagnosis.",
+)
+
+iface.launch()