CesarLeblanc commited on
Commit
f37b5da
1 Parent(s): d8018cd

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +7 -15
app.py CHANGED
@@ -7,14 +7,11 @@ import random
7
  classification_model = pipeline("text-classification", model="plantbert_text_classification_model", tokenizer="plantbert_text_classification_model")
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  mask_model = pipeline("fill-mask", model="plantbert_fill_mask_model", tokenizer="plantbert_fill_mask_model", top_k=14189)
9
 
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- def return_text(habitat_label, habitat_score, confidence):
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- if habitat_score*100 > confidence:
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- text = f"This vegetation plot belongs to the habitat {habitat_label} with the probability {habitat_score*100:.2f}%."
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- else:
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- text = f"We can't assign an habitat to this vegetation plot with a confidence of at least {confidence}%."
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  return text
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- def return_habitat_image(habitat_label, habitat_score, confidence):
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  floraveg_url = f"https://floraveg.eu/habitat/overview/{habitat_label}"
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  response = requests.get(floraveg_url)
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  if response.status_code == 200:
@@ -26,8 +23,6 @@ def return_habitat_image(habitat_label, habitat_score, confidence):
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  image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png"
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  else:
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  image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png"
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- if habitat_score*100 < confidence:
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- image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png"
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  image_url = "https://www.commissionoceanindien.org/wp-content/uploads/2018/07/plantnet.jpg"
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  image = gr.Image(value=image_url)
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  return image
@@ -71,13 +66,12 @@ def gbif_normalization(text):
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  text = text.lower()
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  return text
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- def classification(text, typology, confidence):
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  text = gbif_normalization(text)
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  result = classification_model(text)
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  habitat_label = result[0]['label']
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- habitat_score = result[0]['score']
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- formatted_output = return_text(habitat_label, habitat_score, confidence)
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- image_output = return_habitat_image(habitat_label, habitat_score, confidence)
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  return formatted_output, image_output
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83
  def masking(text):
@@ -159,14 +153,12 @@ with gr.Blocks() as demo:
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  with gr.Row():
160
  with gr.Column():
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  species = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.")
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- typology = gr.Dropdown(["EUNIS"], value="EUNIS", label="Typology", info="Will add more typologies later!")
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- confidence = gr.Slider(0, 100, value=90, label="Confidence", info="Choose the level of confidence for the prediction.")
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  with gr.Column():
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  text_output_1 = gr.Textbox()
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  text_output_2 = gr.Image()
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  text_button = gr.Button("Classify")
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  gr.Markdown("""<h5 style="text-align: center;">An example of input</h5>""")
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- gr.Examples([["sparganium erectum, calystegia sepium, persicaria amphibia", "EUNIS", 90]], [species, typology, confidence], [text_output_1, text_output_2], classification, True)
170
 
171
  with gr.Tab("Missing species finding"):
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  gr.Markdown("""<h3 style="text-align: center;">Finding the missing species!</h3>""")
 
7
  classification_model = pipeline("text-classification", model="plantbert_text_classification_model", tokenizer="plantbert_text_classification_model")
8
  mask_model = pipeline("fill-mask", model="plantbert_fill_mask_model", tokenizer="plantbert_fill_mask_model", top_k=14189)
9
 
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+ def return_text(habitat_label):
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+ text = f"This vegetation plot belongs to the habitat {habitat_label}."
 
 
 
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  return text
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+ def return_habitat_image(habitat_label):
15
  floraveg_url = f"https://floraveg.eu/habitat/overview/{habitat_label}"
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  response = requests.get(floraveg_url)
17
  if response.status_code == 200:
 
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  image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png"
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  else:
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  image_url = "https://www.salonlfc.com/wp-content/uploads/2018/01/image-not-found-scaled-1150x647.png"
 
 
26
  image_url = "https://www.commissionoceanindien.org/wp-content/uploads/2018/07/plantnet.jpg"
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  image = gr.Image(value=image_url)
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  return image
 
66
  text = text.lower()
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  return text
68
 
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+ def classification(text):
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  text = gbif_normalization(text)
71
  result = classification_model(text)
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  habitat_label = result[0]['label']
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+ formatted_output = return_text(habitat_label)
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+ image_output = return_habitat_image(habitat_label)
 
75
  return formatted_output, image_output
76
 
77
  def masking(text):
 
153
  with gr.Row():
154
  with gr.Column():
155
  species = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.")
 
 
156
  with gr.Column():
157
  text_output_1 = gr.Textbox()
158
  text_output_2 = gr.Image()
159
  text_button = gr.Button("Classify")
160
  gr.Markdown("""<h5 style="text-align: center;">An example of input</h5>""")
161
+ gr.Examples([["sparganium erectum, calystegia sepium, persicaria amphibia"]], [species], [text_output_1, text_output_2], classification, True)
162
 
163
  with gr.Tab("Missing species finding"):
164
  gr.Markdown("""<h3 style="text-align: center;">Finding the missing species!</h3>""")