Update app.py

app.py

@@ -1,16 +1,17 @@
+
 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 
+from fastai.vision.all import load_learner  # Corrected import
 
 # Initialize Streamlit app
 st.title("White Blood Cell Classifier")
 
 # Load the FastAI model for WBC identification
-fastai_model = load_learner('model1.pkl')  # Model for WBC classification
+fastai_model = load_learner('model1.pkl')
 
 # File uploader for image input
 uploaded_file = st.file_uploader("Upload an image for classification", type=["jpg", "png"])
@@ -19,17 +20,35 @@ if uploaded_file:
     # Open the uploaded image
     image = Image.open(uploaded_file)
     
-    # Display the uploaded image
+    # Display the uploaded image with a caption
     st.image(image, caption="Uploaded Image", use_column_width=True)
 
     # Perform inference with the FastAI model
     pred, idx, probs = fastai_model.predict(image)
-    
-    # Display White Blood Cell Classification Results
-    st.write("White Blood Cell Classification:")
-    categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')  # WBC types
-    results_dict = dict(zip(categories, map(float, probs)))
-    st.write(results_dict)
+
+    # Display a title for the results section
+    st.subheader("White Blood Cell Classification Results")
+
+    # Define categories for classification
+    categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
+
+    # Create a DataFrame with classification probabilities
+    results_df = pd.DataFrame(
+        {'Cell Type': categories, 'Probability': probs.tolist()}
+    )
+
+    # Display the 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 the probabilities
+    st.write("Detailed Classification Results:")
+    st.table(results_df)
 
 else:
-    st.write("Upload an image to start classification.")
+    st.warning("Upload an image to start classification.")
+
+