Create app.py

app.py

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+import streamlit as st
+import os
+from werkzeug.utils import secure_filename
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# Load the trained model
+model = load_model("Bone_fracture_classifier_model.h5")
+
+# Function to check if the file extension is allowed
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'jpg', 'jpeg', 'png'}
+
+# Function to preprocess the image
+def preprocess_image(file_path):
+    img = image.load_img(file_path, target_size=(200, 200))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0  # Normalize the image
+    return img_array
+
+def main():
+    st.title("Bone Fracture Detection App")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Check if the file extension is allowed
+        if allowed_file(uploaded_file.name):
+            # Display the selected image
+            st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+            # Save the uploaded image temporarily
+            temp_image_path = "temp_image.jpg"
+            with open(temp_image_path, "wb") as temp_image:
+                temp_image.write(uploaded_file.read())
+
+            # Preprocess the image
+            img_array = preprocess_image(temp_image_path)
+
+            # Make prediction
+            prediction = model.predict(img_array)[0, 0]
+            result = "Broken" if prediction > 0.5 else "Not Broken"
+
+            st.write(f"Prediction: {result}")
+            st.write(f"Confidence: {prediction:.2%}")
+
+            # Remove the temporary image file
+            os.remove(temp_image_path)
+        else:
+            st.warning("Invalid file format. Please upload an image with a valid format (jpg, jpeg, or png).")
+
+if __name__ == "__main__":
+    main()