Create app.py

app.py

@@ -0,0 +1,26 @@
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Load a flower classification model from Hugging Face or your custom model
+flower_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")
+
+st.title("Flower Identifier 🌸")
+
+# Upload the image
+file_name = st.file_uploader("Upload a flower image")
+
+if file_name is not None:
+    col1, col2 = st.columns(2)
+
+    # Display the uploaded image
+    image = Image.open(file_name)
+    col1.image(image, use_column_width=True)
+    
+    # Make predictions
+    predictions = flower_pipeline(image)
+
+    # Display probabilities
+    col2.header("Predictions 🌼")
+    for p in predictions:
+        col2.subheader(f"{p['label']}: {round(p['score'] * 100, 1)}%")