Upload 3 files

CModel.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:618cb495f0a92837c9d5c3a8be8982e2801195d57c065bfb82726f1568d70f12
+size 8411264

app.py

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+import tensorflow as tf
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# ----------------------------
+# LOAD MODEL (LOCAL FILE)
+# ----------------------------
+model = tf.keras.models.load_model("CModel.h5")
+
+IMG_SIZE = (224, 224)
+
+CLASS_NAMES = [
+    "Normal",
+    "Monkeypox"
+]
+
+# ----------------------------
+# PREDICTION FUNCTION
+# ----------------------------
+def predict_image(image):
+    image = image.convert("RGB")
+    image = image.resize(IMG_SIZE)
+
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    pred = model.predict(img_array)
+
+    if pred.shape[1] == 1:
+        confidence = float(pred[0][0])
+        label = CLASS_NAMES[1] if confidence > 0.5 else CLASS_NAMES[0]
+        return label, confidence
+    else:
+        class_index = int(np.argmax(pred))
+        confidence = float(pred[0][class_index])
+        return CLASS_NAMES[class_index], confidence
+
+# ----------------------------
+# GRADIO UI
+# ----------------------------
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(label="Prediction"),
+        gr.Number(label="Confidence")
+    ],
+    title="Monkeypox Classification using CNN",
+    description="Upload a skin image to classify Monkeypox using a CNN model."
+)
+
+interface.launch()

requirements (1).txt

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+tensorflow
+numpy
+pillow
+gradio