Create app.py

app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+import os
+import time
+import requests
+from io import BytesIO
+
+# Function to load the model (cached for efficiency)
+@st.cache_resource
+def load_vgg_model():
+    return load_model("tuned_ai_model_best_lat.model.keras")
+
+# Function to run the prediction and show progress
+def run_prediction(image_path, model, img_size):
+    # Create a progress bar and status text
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+
+    for i in range(100):
+        progress_bar.progress(i + 1)
+        status_text.text(f"Processing... {i+1}%")
+        time.sleep(0.01)
+
+    # Process and predict
+    try:
+        # Check if the input is a file-like object or a string path
+        if isinstance(image_path, str):
+            image = Image.open(image_path).convert("RGB")
+        else:
+            image = Image.open(image_path).convert("RGB")
+
+        img_array = np.array(image.resize((img_size, img_size)))
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0
+
+        prediction = model.predict(img_array)
+        class_predicted = (prediction > 0.5).astype("int32")[0][0]
+
+        # Display the result
+        if class_predicted == 1:
+            st.error(f"Prediction: Covid Detected (Probability: {100*prediction[0][0]:.2f}%)")
+        else:
+            st.success(f"Prediction: No Covid Detected (Probability: {100*(1 - prediction[0][0]):.2f}%)")
+    except Exception as e:
+        st.error(f"An error occurred during classification: {e}")
+
+    # Clear the progress bar
+    progress_bar.empty()
+    status_text.empty()
+
+# Streamlit UI
+st.title("Covid Detection App")
+st.write("Upload your own image to get a prediction.")
+
+# Load the model
+model = load_vgg_model()
+
+# Define image size (must match the model's input size)
+img_size = 150
+
+# --- UI for user image upload ---
+st.subheader("Upload Your Own Image")
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    st.image(uploaded_file, caption='Uploaded MRI Scan', use_container_width=True)
+
+    # Use a button to trigger the classification explicitly
+    if st.button("Check for Covid"):
+        run_prediction(uploaded_file, model, img_size)