Update app.py

app.py

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