Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+
+# Load pre-trained ResNet50 model
+model = tf.keras.models.load_model("resnet50_kidney_ct_augmented.h5")  # Update this path if you are using a .pb file
+
+# Class labels (change based on your model's labels)
+labels = ["Cyst", "Normal", "Stone", "Tumor"]
+
+def predict(img):
+    # Resize and preprocess image to fit ResNet50 input format
+    img = img.resize((224, 224))  # ResNet50 expects 224x224 images
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    # Model prediction
+    prediction = model.predict(img_array)
+    predicted_class = np.argmax(prediction, axis=1)
+    
+    return labels[predicted_class[0]]
+
+# Streamlit interface
+st.title("TensorFlow Image Classification with ResNet50")
+st.write("Upload an image to classify")
+
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    img = Image.open(uploaded_image)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+    
+    # Make prediction
+    prediction = predict(img)
+    st.write(f"Prediction: {prediction}")