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Update app.py
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app.py
CHANGED
@@ -1,3 +1,4 @@
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import streamlit as st
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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@@ -20,24 +21,54 @@ question_completion_pipeline = pipeline(
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device=-1
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)
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# Streamlit UI
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st.title("Question Completion Model")
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st.write("
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if st.button("Complete Question"):
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if partial_question.strip():
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partial_question,
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max_length=60,
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num_return_sequences=
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do_sample=True
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)
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else:
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st.warning("Please enter a partial question.")
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@@ -58,4 +89,3 @@ if st.button("Complete Question"):
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import streamlit as st
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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device=-1
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)
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# Suggested partial questions
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suggested_questions = [
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"What is the impact of",
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"How does the company report",
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"What are the financial risks of",
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"Explain the corporate governance of",
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"What was the revenue growth of"
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]
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# Streamlit UI
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st.title("Question Completion Model")
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st.write("Enter a partial question related to financial statements, corporate governance, or company reports, and the app will intelligently complete it based on learned patterns!")
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# Session state for input question
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if "partial_question" not in st.session_state:
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st.session_state.partial_question = ""
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# Function to update input when a suggestion is clicked
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def update_input(selected_question):
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st.session_state.partial_question = selected_question
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st.rerun()
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# Display suggested partial questions
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st.write("### Suggested Partial Questions:")
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cols = st.columns(5)
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for i, question in enumerate(suggested_questions):
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if cols[i % 5].button(question, key=f"suggested_{i}"): # Place buttons in columns
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update_input(question)
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# Text input box
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partial_question = st.text_input("Enter a partial question:", st.session_state.partial_question)
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# Button to generate 3 completed questions
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if st.button("Complete Question"):
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if partial_question.strip():
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outputs = question_completion_pipeline(
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partial_question,
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max_length=60,
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num_return_sequences=3, # Generate 3 different completions
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do_sample=True,
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truncation=True
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)
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st.write("### Completed Questions:")
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for output in outputs:
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completed_question = output["generated_text"]
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st.markdown(f"**{completed_question}**") # Display non-clickable text
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else:
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st.warning("Please enter a partial question.")
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