Create app.py

app.py

@@ -0,0 +1,58 @@
+import streamlit as st
+from transformers import pipeline
+import requests
+from io import BytesIO
+from PIL import Image
+
+# Define models and their validation accuracies
+model_options = {
+    "Model Name": {
+        "path": "model_name.h5",
+        "accuracy": 50
+    },
+    "Old Model": {
+        "path": "oldModel.h5",
+        "accuracy": 76
+    }
+}
+
+# Load the model from Hugging Face repo
+def load_model(model_path):
+    # Here you would use the Hugging Face `transformers` library to load your model.
+    # However, since these are `.h5` models (likely Keras models), use the appropriate loader.
+    # This example assumes you have a custom loader function for Keras models.
+    from tensorflow.keras.models import load_model
+    return load_model(model_path)
+
+def main():
+    st.title("Pneumonia Detection App")
+
+    model_name = st.selectbox("Select a model", list(model_options.keys()))
+    model_path = model_options[model_name]["path"]
+    model_accuracy = model_options[model_name]["accuracy"]
+
+    # Load the selected model
+    model = load_model(model_path)
+    
+    st.write(f"Model: {model_name}")
+    st.write(f"Validation Accuracy: {model_accuracy}%")
+
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Perform prediction using the model
+        # This part depends on how your model expects input.
+        # Here, you would preprocess the image and perform prediction.
+        # For example:
+        # img_array = preprocess_image(image)
+        # prediction = model.predict(img_array)
+        # st.write("Prediction:", prediction)
+
+        # Example placeholder for prediction output
+        st.write("Prediction: [Placeholder for actual prediction]")
+
+if __name__ == "__main__":
+    main()