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app.py

@@ -2,40 +2,77 @@ import streamlit as st
 import numpy as np
 from PIL import Image
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import os
+import sys
+import platform
+import subprocess
 
-# Load the trained VGG16 model
+# Add a function to load the model
 @st.cache_resource
 def load_vgg_model():
     return load_model("brain_tumor_vgg16_model.keras")
 
-model = load_vgg_model()
-img_size = 150
-
 # Streamlit UI
 st.title("Brain Tumor MRI Classification App")
 st.write("Upload a brain MRI scan to check if it contains a tumor.")
 
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Display the uploaded image
-    image = Image.open(uploaded_file).convert("RGB")
-    st.image(image, caption='Uploaded MRI Scan', use_column_width=True)
-    st.write("")
-    st.write("Classifying...")
-
-    # Preprocess the image for the model
-    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  # Rescale to [0, 1]
-
-    # Make prediction
-    prediction = model.predict(img_array)
-    class_predicted = (prediction > 0.5).astype("int32")[0][0]
-
-    # Display the result
-    if class_predicted == 1:
-        st.error("Prediction: Tumor Detected")
-    else:
-        st.success("Prediction: No Tumor Detected")
+# --- DEBUGGING INFORMATION ---
+st.header("Debugging Information")
+
+# Check current working directory
+st.write(f"Current working directory: {os.getcwd()}")
+
+# List files in the directory
+st.write("Files in /app:")
+for file in os.listdir("."):
+    st.write(f"- {file}")
+
+# Display Python and library versions
+st.write("Python version:", sys.version)
+st.write("Numpy version:", np.__version__)
+st.write("TensorFlow version:", tf.__version__)
+st.write("Pillow version:", Image.__version__)
+
+# Display environment variables
+st.write("Environment Variables:")
+for key, value in os.environ.items():
+    st.write(f"- {key}: {value}")
+
+# --- END DEBUGGING INFORMATION ---
+
+# Try to load the model and display success/failure
+try:
+    with st.spinner('Loading model...'):
+        model = load_vgg_model()
+    st.success("Model loaded successfully.")
+    
+    # Define image size (must match the model's input size)
+    img_size = 150
+
+    # UI for image upload
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption='Uploaded MRI Scan', use_column_width=True)
+        st.write("")
+        st.write("Classifying...")
+
+        # Preprocess the image for the model
+        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
+
+        # Make prediction
+        prediction = model.predict(img_array)
+        class_predicted = (prediction > 0.5).astype("int32")[0][0]
+
+        # Display the result
+        if class_predicted == 1:
+            st.error("Prediction: Tumor Detected")
+        else:
+            st.success("Prediction: No Tumor Detected")
+
+except Exception as e:
+    st.error(f"Failed to load or use the model. Error: {e}")