Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-import gradio as gr
+import streamlit as st
 import numpy as np
 import os
 import tensorflow as tf
@@ -10,6 +10,14 @@ from PIL import Image
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# Set page configuration
+st.set_page_config(
+    page_title="Breast Cancer Prediction",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
 # Disable GPU to save memory
 tf.config.set_visible_devices([], 'GPU')
 logger.info("TensorFlow configured for CPU-only")
@@ -17,8 +25,9 @@ logger.info("TensorFlow configured for CPU-only")
 # ===== Model Loading =====
 MODEL_FILE = "final_combined_model.keras"
 
+@st.cache_resource(show_spinner=False)
 def load_model():
-    """Load TensorFlow model from local file"""
+    """Load TensorFlow model from local file with caching"""
     try:
         # Verify file exists
         if not os.path.exists(MODEL_FILE):
@@ -51,7 +60,7 @@ model = load_model()
 def preprocess_image(image):
     """Preprocess image for model prediction"""
     try:
-        # Convert to PIL Image if it's a numpy array
+        # Convert to PIL Image
         if isinstance(image, np.ndarray):
             img = Image.fromarray(image.astype('uint8'))
         else:
@@ -69,7 +78,8 @@ def preprocess_image(image):
         return None
 
 # ===== Prediction Function =====
-def predict(age, tumor_size, image):
+def predict(image):
+    """Make prediction using the loaded model"""
     if model is None:
         return "Model failed to load", "Check logs", None
     
@@ -92,40 +102,117 @@ def predict(age, tumor_size, image):
         logger.error(error_msg)
         return error_msg, "Try again", image
 
-# ===== Gradio Interface =====
-with gr.Blocks(theme=gr.themes.Soft(), title="Breast Cancer Prediction") as demo:
-    # Header
-    gr.Markdown("# 🩺 Breast Cancer Prediction")
-    gr.Markdown("Upload a breast medical image for cancer prediction")
+# ===== Streamlit UI =====
+
+# Custom CSS for styling
+st.markdown("""
+<style>
+    .stApp {
+        background-color: #f0f2f6;
+    }
+    .header {
+        color: #2c3e50;
+        text-align: center;
+        padding: 1rem;
+    }
+    .result-box {
+        border-radius: 10px;
+        padding: 1.5rem;
+        margin: 1rem 0;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+    }
+    .malignant {
+        background-color: #ffcccc;
+        border-left: 5px solid #e74c3c;
+    }
+    .benign {
+        background-color: #ccffcc;
+        border-left: 5px solid #2ecc71;
+    }
+    .stButton>button {
+        background-color: #3498db;
+        color: white;
+        border-radius: 5px;
+        padding: 0.5rem 1rem;
+        width: 100%;
+    }
+    .stButton>button:hover {
+        background-color: #2980b9;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown("<h1 class='header'>🩺 Breast Cancer Prediction</h1>", unsafe_allow_html=True)
+st.markdown("Upload a breast medical image for cancer prediction")
+
+# Status indicator
+status = "✅ Model loaded successfully" if model else "❌ Model failed to load"
+st.info(status)
+
+# Create two columns for layout
+col1, col2 = st.columns([1, 1])
+
+# Input column
+with col1:
+    st.subheader("Patient Information")
     
-    # Status indicator
-    status = gr.Textbox(value="✅ Ready" if model else "❌ Model not loaded", 
-                        label="System Status", interactive=False)
+    # Input fields
+    age = st.number_input("Patient Age", min_value=18, max_value=100, value=45)
+    tumor_size = st.number_input("Tumor Size (mm)", min_value=0.1, value=15.0)
     
-    # Main interface
-    with gr.Row():
-        with gr.Column():
-            inputs = [
-                gr.Number(label="Patient Age", minimum=18, maximum=100, value=45),
-                gr.Number(label="Tumor Size (mm)", minimum=0.1, value=15.0),
-                gr.Image(label="Medical Image", type="filepath", sources=["upload"])
-            ]
-            submit_btn = gr.Button("Analyze", variant="primary")
+    # Image upload
+    uploaded_file = st.file_uploader(
+        "Upload Medical Image", 
+        type=["jpg", "jpeg", "png"],
+        help="Supported formats: JPG, JPEG, PNG"
+    )
+    
+    # Predict button
+    predict_btn = st.button("Analyze Image")
+
+# Results column
+with col2:
+    st.subheader("Prediction Results")
+    
+    # Initialize session state for results
+    if 'result' not in st.session_state:
+        st.session_state.result = None
+        st.session_state.confidence = None
+        st.session_state.image = None
+    
+    # Process image when button is clicked
+    if predict_btn and uploaded_file is not None:
+        try:
+            image = Image.open(uploaded_file)
+            st.session_state.result, st.session_state.confidence, st.session_state.image = predict(image)
+        except Exception as e:
+            st.error(f"Error processing image: {str(e)}")
+    
+    # Display results if available
+    if st.session_state.result:
+        # Result box with color coding
+        result_class = "malignant" if st.session_state.result == "Malignant" else "benign"
+        st.markdown(
+            f"<div class='result-box {result_class}'>"
+            f"<h3>Diagnosis: {st.session_state.result}</h3>"
+            f"<p>Confidence: {st.session_state.confidence}</p>"
+            "</div>", 
+            unsafe_allow_html=True
+        )
         
-        with gr.Column():
-            outputs = [
-                gr.Label(label="Diagnosis"),
-                gr.Label(label="Confidence"),
-                gr.Image(label="Preview", interactive=False)
-            ]
+        # Display image
+        if st.session_state.image:
+            st.image(
+                st.session_state.image, 
+                caption="Uploaded Image",
+                use_container_width=True
+            )
     
-    # Callback
-    submit_btn.click(
-        fn=predict,
-        inputs=inputs,
-        outputs=outputs
-    )
+    # Show placeholder if no results
+    elif not predict_btn:
+        st.info("Upload an image and click 'Analyze Image' to get prediction")
 
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+# Footer
+st.markdown("---")
+st.caption("This tool is for research purposes only. Consult a medical professional for clinical diagnosis.")