.

app.py

@@ -3,45 +3,74 @@
 
 # Voila runs jpnb but hides the code cells and only displays the output (including the ipywidgets) as well as the markdown cells.
 
-import fastbook
-fastbook.setup_book()
-from fastbook import *
+import streamlit as st
+from fastai.vision.all import *
 from fastai.vision.widgets import *
 
 # Load the model
 path = Path()
-path.ls(file_exts='.pkl')
-
 learn_inf = load_learner(path/'export.pkl')
 
-# Create a button widget
-btn_upload = widgets.FileUpload()
-btn_upload
+# Title of the app
+st.title('Forest Image Classifier')
+
+# File uploader
+uploaded_file = st.file_uploader("Select your forest image", type=['jpg', 'jpeg', 'png'])
+
+# If an image has been uploaded
+if uploaded_file is not None:
+    # Display the uploaded image
+    img = PILImage.create(uploaded_file)
+    st.image(img.to_thumb(128, 128), caption='Uploaded Image', use_column_width=True)
+
+    # Classify the image
+    pred, pred_idx, probs = learn_inf.predict(img)
+    
+    # Display the prediction and probability
+    st.write(f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}')
+
+
+# 
+# 
+# import fastbook
+# fastbook.setup_book()
+# from fastbook import *
+# from fastai.vision.widgets import *
+
+# # Load the model
+# path = Path()
+# path.ls(file_exts='.pkl')
+
+# learn_inf = load_learner(path/'export.pkl')
+
+# # Create a button widget
+# btn_upload = widgets.FileUpload()
+# btn_upload
 
-# Create a clear_output widget
-out_pl = widgets.Output()
-out_pl.clear_output()
+# # Create a clear_output widget
+# out_pl = widgets.Output()
+# out_pl.clear_output()
 
-# Create a label widget
-lbl_pred = widgets.Label()
+# # Create a label widget
+# lbl_pred = widgets.Label()
 
-# Create a run button widget
-btn_run = widgets.Button(description='Classify')
-btn_run
+# # Create a run button widget
+# btn_run = widgets.Button(description='Classify')
+# btn_run
 
-# Function to classify the image
-def on_click_classify(change):
-    img = PILImage.create(btn_upload.data[-1])
-    out_pl.clear_output()
-    with out_pl: display(img.to_thumb(128,128))
-    pred,pred_idx,probs = learn_inf.predict(img)
-    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+# # Function to classify the image
+# def on_click_classify(change):
+#     img = PILImage.create(btn_upload.data[-1])
+#     out_pl.clear_output()
+#     with out_pl: display(img.to_thumb(128,128))
+#     pred,pred_idx,probs = learn_inf.predict(img)
+#     lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
 
-btn_run.on_click(on_click_classify)
+# btn_run.on_click(on_click_classify)
 
-# Create a Vertical Box (VBox) to hold the widgets
-btn_upload = widgets.FileUpload()
-VBox([widgets.Label('Select your forest!'),
-      btn_upload, btn_run, out_pl, lbl_pred])
+# # Create a Vertical Box (VBox) to hold the widgets
+# btn_upload = widgets.FileUpload()
+# VBox([widgets.Label('Select your forest!'),
+#       btn_upload, btn_run, out_pl, lbl_pred])