Update app.py

app.py

@@ -3,32 +3,24 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
-# Load trained models
-diabetes_model = tf.keras.models.load_model("diabetes_model.h5")
+# Load trained pneumonia model
 pneumonia_model = tf.keras.models.load_model("pneumonia_model.h5")
 
-def predict_disease(image_path, disease):
+def predict_pneumonia(image_path):
     img = Image.open(image_path).convert("RGB").resize((128, 128))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
 
-    if disease == "Diabetes":
-        model = diabetes_model
-    elif disease == "Pneumonia":
-        model = pneumonia_model
-    else:
-        return "Invalid disease selected"
-
-    prediction = model.predict(img_array)
+    prediction = pneumonia_model.predict(img_array)
     return "Positive" if prediction[0][0] > 0.5 else "Negative"
 
 # Gradio Interface
 interface = gr.Interface(
-    fn=predict_disease,
-    inputs=[gr.Image(type="filepath"), gr.Radio(["Diabetes", "Pneumonia"])],
+    fn=predict_pneumonia,
+    inputs=gr.Image(type="filepath"),
     outputs="text",
-    title="Disease Prediction",
-    description="Upload an image and select a disease to get a prediction."
+    title="Pneumonia Prediction",
+    description="Upload an image to get a pneumonia prediction."
 )
 
 # Launch app