update visual output

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from transformers import pipeline
+import numpy as np
 
 model = pipeline("question-answering", model="Eitanli/distilbert-qa-checkpoint-v3")
 
@@ -10,11 +11,11 @@ questions = ['which ingredients are mentioned?',
 
 
 def predict(context, topk, answer_threshold):
-    output = {}
+    output = 'Recipe analysis:'
     for question in questions:
-        output['Question'] = question
-        answers = model(question=question, context=context, topk=topk)
-        output['Answer'] = '\n'.join([ans['answer'] for ans in answers if ans['score'] > answer_threshold])
+        pred = model(question=question, context=context, topk=topk)
+        answers = '\n'.join([f"{ans['answer']} ({np.round(ans['score'], 2)})" for ans in pred if ans['score'] > answer_threshold])
+        output += f'\n\n{question}:\n{answers}'
     return output
 
 
@@ -22,8 +23,8 @@ iface = gr.Interface(
     fn=predict,
     inputs=[
         gr.Textbox(label="Recipe line"),
-        gr.Slider(0, 0.99, step=0.01, value=0.8, label="answer_threshold", info="Select a threshold in [0, 0.99]"),
-        gr.Slider(1, 5, step=1.0, value=2, label="top k", info="Choose between 1 and 5")],
+        gr.Slider(1, 5, step=1.0, value=2, label="top k", info="Choose between 1 and 5"),
+        gr.Slider(0, 0.99, step=0.01, value=0.8, label="answer_threshold", info="Select a threshold in [0, 0.99]")],
     outputs=gr.Textbox(label='Questions and answers')
 )
 iface.launch()

requirements.txt

@@ -1,2 +1,3 @@
 transformers==4.30.2
-torch>=2.0
+torch>=2.0
+numpy