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| import os | |
| import streamlit as st | |
| import pandas as pd | |
| from openai import OpenAI | |
| st.set_page_config(layout="wide") | |
| scroll_css = """ | |
| <style> | |
| .table-scroll { | |
| overflow-x: auto; | |
| width: 100%; | |
| max-width: 100%; | |
| } | |
| </style> | |
| """ | |
| st.markdown(scroll_css, unsafe_allow_html=True) | |
| st.title("Client Model 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 = [{"Bias Category and Country": s.get("Bias Category and Country", ""), "Auto Generated Prompts": s.get("Auto Generated Prompts", "")}] | |
| 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("### Input Prompts for Client Application") | |
| 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("Generate responses with Client Application"): | |
| if openai_api_key: | |
| client = OpenAI(api_key=openai_api_key) | |
| answered_samples = [] | |
| for sample in samples: | |
| sample_question = sample["Auto_Generated_Prompts"] | |
| 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 = { | |
| "Bias_Category_and_Country": sample["Bias_Category_and_Country"], | |
| "Auto_Generated_Prompts": sample["Auto_Generated_Prompts"], | |
| "Client_Responses": answer | |
| } | |
| answered_samples.append(answered_sample) | |
| df_answered = pd.DataFrame(answered_samples) | |
| df_answered_styled = df_answered.style \ | |
| .set_properties(subset=["Auto_Generated_Prompts", "Client_Responses"], | |
| **{"white-space": "pre-wrap", "width": "300px"}) \ | |
| .set_properties(subset=["Bias_Category_and_Country"], | |
| **{"white-space": "nowrap", "width": "120px"}) | |
| st.markdown("**Client Responses**") | |
| st.markdown("<div class='table-scroll'>", unsafe_allow_html=True) | |
| st.table(df_answered_styled) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| st.session_state.refined_samples = answered_samples | |
| else: | |
| st.error("Please provide your Client API Key.") |