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import os
from dotenv import load_dotenv
load_dotenv()
import streamlit as st
import pandas as pd
from openai import OpenAI
st.title("Client Response (Answering)")
# Use best_samples if available; otherwise, fallback to the interactive single sample.
if "best_samples" in st.session_state:
samples = st.session_state.best_samples
elif "single_sample" in st.session_state:
s = st.session_state.single_sample
# Rename keys: "question" becomes "prompt" and "response" becomes "question"
samples = [{"prompt": s.get("question", ""), "question": s.get("response", "")}]
elif "generated_text" in st.session_state and "prompt_text" in st.session_state:
samples = [{"prompt": st.session_state.prompt_text, "question": st.session_state.generated_text}]
else:
st.error("No samples found. Please generate samples on the main page first.")
st.stop()
st.markdown("### Samples for Answering")
df_samples = pd.DataFrame(samples)
st.dataframe(df_samples)
default_openai_key = os.getenv("OPENAI_API_KEY") or ""
openai_api_key = st.text_input("Enter your Client API Key", type="password", value=default_openai_key)
if st.button("Answer Samples with Client Model"):
if openai_api_key:
client = OpenAI(api_key=openai_api_key)
answered_samples = []
for sample in samples:
sample_question = sample["question"]
prompt = (
f"Answer the following question comprehensively and concisely:\n\n"
f"{sample_question}\n\n"
"Provide a clear, one-sentence answer."
)
completion = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}]
)
answer = completion.choices[0].message.content.strip()
answered_sample = {
"prompt": sample["prompt"],
"question": sample["question"],
"answer": answer
}
answered_samples.append(answered_sample)
st.markdown("**Answered Samples:**")
df_answered = pd.DataFrame(answered_samples)
st.dataframe(df_answered)
st.session_state.refined_samples = answered_samples
else:
st.error("Please provide your Client API Key.") |