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Create app.py
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app.py
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import streamlit as st
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import json
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import pandas as pd
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import plotly.express as px
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# Define categories
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CATEGORIES = ["Writing", "Roleplay", "Reasoning", "Math", "Coding", "Extraction", "STEM", "Humanities"]
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# Load and process the single model data
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@st.cache(allow_output_mutation=True)
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def get_model_df():
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q2result = []
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# Replace "gpt-4_single.jsonl" with the actual path to your JSONL file
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with open("gpt-4_single.jsonl", "r") as fin:
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for line in fin:
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obj = json.loads(line)
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obj["category"] = CATEGORIES[(obj["question_id"] - 81) // 10]
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q2result.append(obj)
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df = pd.DataFrame(q2result)
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return df
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# Placeholder for the pair model data function
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# Adapt this function based on how your "gpt-4_pair.jsonl" is structured
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@st.cache(allow_output_mutation=True)
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def get_model_df_pair():
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# Implement similar to get_model_df if you have pair data
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return pd.DataFrame([]) # Placeholder
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df = get_model_df()
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df_pair = get_model_df_pair()
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# Streamlit app starts here
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st.title('Model Performance Visualization')
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# Select models to display
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all_models = df["model"].unique()
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selected_models = st.multiselect('Select Models', all_models, default=all_models[:3])
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# Main app logic
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if selected_models:
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scores_all = []
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for model in selected_models:
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for cat in CATEGORIES:
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res = df[(df["category"] == cat) & (df["model"] == model) & (df["score"] >= 0)]
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score = res["score"].mean()
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scores_all.append({"model": model, "category": cat, "score": score})
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df_score = pd.DataFrame(scores_all)
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# Renaming models for better visualization
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rename_map = {
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# Define your renaming map here, if needed
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}
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df_score.replace(rename_map, inplace=True)
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# Generate the radial graph
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fig = px.line_polar(df_score, r='score', theta='category', line_close=True,
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category_orders={"category": CATEGORIES}, color='model', markers=True)
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# Display the Plotly figure in Streamlit
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st.plotly_chart(fig)
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st.caption("Note: Make sure to replace placeholder paths with actual paths to your JSONL files.")
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