Update app.py

app.py

@@ -46,7 +46,7 @@ def interpretation_function(text):
 #     return val
 
 def adr_predict(x):
-    encoded_input = tokenizer(str(x), return_tensors='pt')
+    encoded_input = tokenizer(x, return_tensors='pt')
     output = model(**encoded_input)
     scores = output[0][0].detach().numpy()
     scores = tf.nn.softmax(scores)
@@ -96,12 +96,9 @@ def main(prob1):
     text = str(prob1).lower()
     obj = adr_predict(text)
     return obj[0],obj[1]
-    # ,obj[2]
 
 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.
-"""
+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."""
 
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
@@ -135,9 +132,7 @@ with gr.Blocks(title=title) as demo:
     )
 
     gr.Markdown("### Click on any of the examples below to see to what extent they contain resilience messaging:")
-    gr.Examples([["I have minor pain."],["I have severe pain."]], [prob1], [label
-                                                                           # ,intp
-                                                                           ,interpretation
+    gr.Examples([["I have minor pain."],["I have severe pain."]], [prob1], [label,interpretation
                                                                           ], main, cache_examples=True)
     
 demo.launch()