Create stremlit_app.py

stremlit_app.py

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+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+
+# Load the pre-trained model
+classifier = pipeline("image-classification", model="https://teachablemachine.withgoogle.com/models/lcNO3nb0s/")
+
+st.title("Korean Jelly Identifier")
+
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Classify the image
+    results = classifier(image)
+
+    jelly_type = results[0]['label']
+    sugar_level = get_sugar_level(jelly_type)
+    hazard = get_hazard_level(sugar_level)
+
+    st.write(f'Jelly Type: {jelly_type}')
+    st.write(f'Sugar Level: {sugar_level}')
+    st.write(f'Hazard: {hazard}')
+
+def get_sugar_level(jelly_type):
+    # Dummy data for demonstration purposes
+    sugar_data = {
+        'jellyA': 10,
+        'jellyB': 20,
+        'jellyC': 30
+    }
+    return sugar_data.get(jelly_type, 0)
+
+def get_hazard_level(sugar_level):
+    if sugar_level > 25:
+        return 'Red (High Hazard)'
+    elif sugar_level > 15:
+        return 'Yellow (Moderate Hazard)'
+    else:
+        return 'Green (Low Hazard)'