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import streamlit as st
from transformers import pipeline
# model repo ID
model_id = "prd101-wd/phi1_5-bankingqa-merged"
# Load model only once
@st.cache_resource
def load_model():
#return pipeline("question-answering", model=model_id)
return pipeline("text-generation", model=model_id, trust_remote_code=True)
# Create a text generation pipeline
pipe = load_model()
# Streamlit UI
st.title("Banking HelpDesk from Finetuned Phi1-5")
st.markdown("Ask a question and the fine-tuned Phi-1.5 model will answer.")
user_input = st.text_area("Your question:", height=100)
if st.button("Ask"):
if user_input.strip():
# Format the prompt like Alpaca-style
prompt = f"### Instruction:\n{user_input}\n\n### Response:\n"
output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.7)
# Process output
if isinstance(output, list) and output:
answer = output[0]['generated_text']
# Extract only the response part
if "### Response:" in answer:
answer = answer.split("### Response:")[-1].strip()
else:
answer = "Unable to generate a response. Please try again."
# if isinstance(output, list) and len(output) > 0 and "generated_text" in output[0]:
# answer = output[0]["generated_text"]
# else:
# answer = "Unable to generate a response. Please try again."
# Extract only the model's response (remove prompt part if included in output)
#answer = output.split("### Response:")[-1].strip()
# if isinstance(output, str):
# answer = output.split("### Response:")[-1].strip()
# else:
# answer = "Unexpected output format. Please try again."
st.markdown("### HelpdeskBot Answer:")
st.success(answer)
else:
st.warning("Please enter a question.")
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