stable version of the deepforest demo

app.py

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+import numpy as np
+import gradio as gr
+from deepforest import main
+from deepforest import get_data
+import os
+import matplotlib.pyplot as plt
+import rasterio
+from PIL import Image
+
+#img = model.predict_image(path="/Users/benweinstein/Documents/NeonTreeEvaluation/evaluation/RGB/TEAK_049_2019.tif",return_plot=True)
+
+#predict_image returns plot in BlueGreenRed (opencv style), but matplotlib likes RedGreenBlue, switch the channel order.
+def predictDeepForest(input_img):
+    model = main.deepforest()
+    model.use_release()
+    #print(input_img.shape)
+    im = Image.open(input_img) 
+    im = np.array(im)
+    img = model.predict_image(im, return_plot=True)
+    img.shape
+    plt.imshow(img[:,:,::-1])
+    return img
+#gr.Image(type="filepath", shape=...)
+
+demo = gr.Interface(predictDeepForest, gr.Image(type="filepath"), "image")
+demo.launch(debug = True)

requirements.txt

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+deepforest
+gradio
+rasterio
+matplotlib
+PIL