Update app.py

app.py

@@ -1,28 +1,28 @@
-import gradio as gr
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-
-# Load your saved model (.keras file)
-model = load_model("malaria_model.keras")
-
-IMG_SIZE = (64, 64)   # matches your training
-
-def predict(img):
-    img = img.convert("RGB")
-    img = img.resize(IMG_SIZE)
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = img_array / 255.0  # normalization
-
-    prediction = model.predict(img_array)[0][0]
-    label = "Infected" if prediction > 0.5 else "Uninfected"
-    return label
-
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="Malaria Cell Image Predictor",
-    description="Upload a blood smear cell image to detect malaria (Infected/Uninfected)."
-).launch()
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# Load your saved model (.keras file)
+model = load_model("malaria_model.keras")
+
+IMG_SIZE = (64, 64)   # matches your training
+
+def predict(img):
+    img = img.convert("RGB")
+    img = img.resize(IMG_SIZE)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0  # normalization
+
+    prediction = model.predict(img_array)[0][0]
+    label = "Infected" if prediction > 0.5 else "Uninfected"
+    return label
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Malaria Cell Image Predictor",
+    description="Upload a blood smear cell image to detect malaria (Infected/Uninfected)."
+).launch(share=True)