Update app.py

app.py

@@ -1,11 +1,11 @@
-
 import streamlit as st
 import PIL.Image as Image
 import numpy as np
 import pandas as pd
 import requests
 from io import BytesIO
-from fastai.vision.all import load_learner  # Corrected import
+from fastai.vision.all import load_learner, untar_data, URLs
+import gradio as gr
 
 # Initialize Streamlit app
 st.title("White Blood Cell Classifier")
@@ -13,42 +13,55 @@ st.title("White Blood Cell Classifier")
 # Load the FastAI model for WBC identification
 fastai_model = load_learner('model1.pkl')
 
-# File uploader for image input
-uploaded_file = st.file_uploader("Upload an image for classification", type=["jpg", "png"])
+# Pre-load some example images with their corresponding labels
+example_images = {
+    "Eosinophil": "Eosinophil.jpg",
+    "Lymphocyte": "Lymphocyte.jpg",
+    "Monocyte": "Monocyte.jpg",
+    "Neutrophil": "Neutrophil.jpg",
+}
 
-if uploaded_file:
-    # Open the uploaded image
-    image = Image.open(uploaded_file)
-    
-    # Display the uploaded image with a caption
-    st.image(image, caption="Uploaded Image", use_column_width=True)
+# Provide a choice of example images
+st.sidebar.header("Example Images")
+example_choice = st.sidebar.selectbox(
+    "Select an example image to classify",
+    list(example_images.keys())
+)
 
-    # Perform inference with the FastAI model
-    pred, idx, probs = fastai_model.predict(image)
+# Display the chosen example image
+example_url = example_images[example_choice]
+response = requests.get(example_url)
+example_image = Image.open(BytesIO(response.content))
+st.sidebar.image(example_image, caption=f"Example: {example_choice}", use_column_width=True)
+
+# File uploader for image input
+uploaded_file = st.file_uploader("Upload your own image for classification", type=["jpg", "png"])
 
-    # Display a title for the results section
-    st.subheader("White Blood Cell Classification Results")
+# Check if an image has been uploaded or an example has been selected
+image_to_classify = example_image if uploaded_file is None else Image.open(uploaded_file)
 
-    # Define categories for classification
-    categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
+# Display the image being classified
+st.image(image_to_classify, caption="Image for Classification", use_column_width=True)
 
-    # Create a DataFrame with classification probabilities
-    results_df = pd.DataFrame(
-        {'Cell Type': categories, 'Probability': probs.tolist()}
-    )
+# Perform inference with the FastAI model
+pred, idx, probs = fastai_model.predict(image_to_classify)
 
-    # Display the probabilities as a bar chart
-    st.bar_chart(results_df.set_index('Cell Type'))
+# Display a subheader for results
+st.subheader("White Blood Cell Classification Results")
 
-    # Highlight the most likely class
-    most_likely_class = categories[idx]
-    st.success(f"Predicted Class: {most_likely_class}")
+# Define categories for classification
+categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
 
-    # Additional information about the probabilities
-    st.write("Detailed Classification Results:")
-    st.table(results_df)
+# Create a DataFrame with classification probabilities
+results_df = pd.DataFrame({'Cell Type': categories, 'Probability': probs.tolist()})
 
-else:
-    st.warning("Upload an image to start classification.")
+# Display probabilities as a bar chart
+st.bar_chart(results_df.set_index('Cell Type'))
 
+# Highlight the most likely class
+most_likely_class = categories[idx]
+st.success(f"Predicted Class: {most_likely_class}")
 
+# Additional information about probabilities
+st.write("Detailed Classification Results:")
+st.table(results_df)