Create app.py

app.py

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+streamlit_app_code = """
+import streamlit as st
+from PIL import Image
+import numpy as np
+import keras
+from huggingface_hub import from_pretrained_keras
+
+# Load the pre-trained model for low light image enhancement
+enhancement_model = from_pretrained_keras("ali444/VGG16_finetuned_79", compile=False)
+enhancement_examples = ['examples/_0_1966.png', 'examples/_1_2118.png', 'examples/_1_5031.png']
+
+# Load the pre-trained model for blood cell classification
+classification_model = from_pretrained_keras("ali444/VGG16_finetuned_79", compile=False)
+
+# Define class labels
+class_names = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"]
+
+# Create Streamlit app
+st.title('Blood Cell Classification App')
+
+# Upload an image through Streamlit
+uploaded_file = st.file_uploader("Upload an image...", type="jpg")
+
+if uploaded_file:
+    st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Preprocess the uploaded image
+    image = Image.open(uploaded_file)
+    image = image.resize((150, 150))
+    image_array = np.array(image) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+
+    # Make predictions
+    classification_prediction = classification_model.predict(image_array)
+    predicted_class = class_names[np.argmax(classification_prediction)]
+
+    # Display the prediction result
+    st.success(f"Prediction: {predicted_class}")
+
+# Add some additional information or instructions
+st.write("")
+st.write("Instructions:")
+st.write("* Upload an image of a blood cell.")
+st.write("* The app will predict the blood cell type.")
+"""