Update app.py

app.py

@@ -3,17 +3,15 @@ import shap
 import numpy as np
 import scipy as sp
 import torch
-# import tensorflow as tf  <-- Removed to match your requirements
 import transformers
 from transformers import pipeline
-from transformers import RobertaTokenizer, RobertaModel
 from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer, AutoModelForTokenClassification
-import matplotlib.pyplot as plt
 import sys
 import csv
 import os
 
+# Environment setup
 HF_TOKEN = os.getenv("hf_token")
 csv.field_size_limit(sys.maxsize)
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
@@ -34,13 +32,13 @@ ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-n
 ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple")
 
 def adr_predict(x):
-    # Ensure input is treated as a string
     text_input = str(x).lower()
     encoded_input = tokenizer(text_input, return_tensors='pt').to(device)
     output = model(**encoded_input)
     
     scores = torch.softmax(output.logits, dim=-1)[0].detach().cpu().numpy()
     
+    # SHAP Explanation
     try:
         shap_values = explainer([text_input])
         local_plot = shap.plots.text(shap_values[0], display=False)
@@ -85,13 +83,14 @@ def adr_predict(x):
 def main(prob1):
     return adr_predict(prob1)
 
+# Gradio Interface
 title = "Welcome to **ADR Detector** 🪐"
-description1 = """This app takes text (up to a few sentences) and predicts to what extent the text describes severe (or non-severe) adverse reaction to medicaitons. Please do NOT use for medical diagnosis."""
+description1 = "This app predicts severe or non-severe adverse reactions to medications. Do NOT use for medical diagnosis."
 
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description1)
-    gr.Markdown("""---""")
+    gr.Markdown("---")
     
     prob1 = gr.Textbox(label="Enter Your Text Here:", lines=2, placeholder="Type it here ...")