Initial commit

app.py

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+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 *
+#from fastai.vision.all import load_learner
+
+# Initialize Streamlit app
+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 detection", type=["jpg", "png"])
+
+if uploaded_file:
+    # Open the uploaded image
+    image = Image.open(uploaded_file)
+    
+    # Perform inference
+    results = model.predict(np.array(image))
+    
+    # Display results
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Render detection results
+    rendered_image = render_result(model=model, image=image, result=results[0])
+    
+    # Show the rendered result
+    st.image(rendered_image, caption="Detection Results", use_column_width=True)
+    
+   
+
+    # Display the counts of each cell type
+    st.write("Cell Type :")
+        # Perform inference with the FastAI model
+    pred, idx, probs = fastai_model.predict(image)
+    st.write("White Blood Cell Classification:")
+    categories = ('EOSINOPHIL', 'LYMPHOCYTE', 'MONOCYTE', 'NEUTROPHIL')
+    results_dict = dict(zip(categories, map(float, probs)))
+    st.write(results_dict)
+else:
+    st.write("Upload an image to start detection.")