app.py

import streamlit as st
import pandas as pd
from PIL import Image, ImageDraw, ImageFont
import io

def main():
    st.markdown(
        """
        <style>
            .stMultiSelect [data-baseweb="tag"] {
                background-color: #3fa45bff !important;
                color: white !important;
                font-weight: medium;
                border-radius: 5px;
                padding: 5px 10px;
            }
            .stMultiSelect [data-baseweb="tag"]:hover {
                background-color: #358d4d !important;
            }
            .stMultiSelect input {
                color: black !important;
            }
        </style>
        """,
        unsafe_allow_html=True,
    )

    with st.sidebar:
        col1, col2 = st.columns([1, 5])
        with col1:
            logo = Image.open("logo.png")
            resized_logo = logo.resize((50, 50))
            st.image(resized_logo)
        with col2:
            st.markdown(
                """
                <div style="display: flex; align-items: center; gap: 10px; margin: 0; padding: 0; font-family: 'Inter', sans-serif; font-size: 26px; font-weight: bold;">
                    AI Energy Score
                </div>
                """,
                unsafe_allow_html=True,
            )
    
    st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
    st.sidebar.write("### Generate Label:")
    
    task_order = [
        "Text Generation", "Image Generation", "Text Classification", "Image Classification", "Image Captioning", 
        "Summarization", "Speech-to-Text (ASR)", "Object Detection", "Question Answering", "Sentence Similarity"
    ]
    
    st.sidebar.write("#### 1. Select task(s) to view models")
    selected_tasks = st.sidebar.multiselect("", options=task_order, default=["Text Generation"])
    
    task_to_file = {
        "Text Generation": "text_gen_energyscore.csv",
        "Image Generation": "image_generation_energyscore.csv",
        "Text Classification": "text_classification_energyscore.csv",
        "Image Classification": "image_classification_energyscore.csv",
        "Image Captioning": "image_caption_energyscore.csv",
        "Summarization": "summarization_energyscore.csv",
        "Speech-to-Text (ASR)": "asr_energyscore.csv",
        "Object Detection": "object_detection_energyscore.csv",
        "Question Answering": "question_answering_energyscore.csv",
        "Sentence Similarity": "sentence_similarity_energyscore.csv"
    }
    
    st.sidebar.write("#### 2. Select a model to generate label")
    default_model_data = {
        'provider': "AI Provider",
        'model': "Model Name",
        'full_model': "AI Provider/Model Name",
        'date': "",
        'task': "",
        'hardware': "",
        'energy': "?",
        'score': 5
    }
    
    if not selected_tasks:
        model_data = default_model_data
    else:
        dfs = []
        for task in selected_tasks:
            file_name = task_to_file[task]
            try:
                df = pd.read_csv(file_name)
            except FileNotFoundError:
                st.sidebar.error(f"Could not find '{file_name}' for task {task}!")
                continue
            except Exception as e:
                st.sidebar.error(f"Error reading '{file_name}' for task {task}: {e}")
                continue

            df['full_model'] = df['model']
            df[['provider', 'model']] = df['model'].str.split(pat='/', n=1, expand=True)
            df['energy'] = (df['total_gpu_energy'] * 1000).round(2)  # Convert to Wh and round to 2 decimal places
            df['score'] = df['energy_score'].fillna(1).astype(int)
            df['date'] = "February 2025"
            df['hardware'] = "NVIDIA H100-80GB"
            df['task'] = task

            dfs.append(df)

        if not dfs:
            model_data = default_model_data
        else:
            data_df = pd.concat(dfs, ignore_index=True)
            if data_df.empty:
                model_data = default_model_data
            else:
                model_options = data_df["full_model"].unique().tolist()
                selected_model = st.sidebar.selectbox(
                    "Scored Models",
                    model_options,
                    help="Start typing to search for a model"
                )
                model_data = data_df[data_df["full_model"] == selected_model].iloc[0]

    st.sidebar.write("#### 3. Download the label")
    try:
        score = int(model_data["score"])
        background_path = f"{score}.png"
        background = Image.open(background_path).convert("RGBA")
    except FileNotFoundError:
        st.sidebar.error(f"Could not find background image '{score}.png'. Using default background.")
        background = Image.open("default_background.png").convert("RGBA")
    except ValueError:
        st.sidebar.error(f"Invalid score '{model_data['score']}'. Score must be an integer.")
        return

    final_size = (520, 728)
    generated_label = background.resize(final_size, Image.Resampling.LANCZOS)
    st.image(generated_label, caption="Generated Label Preview", width=520)

    img_buffer = io.BytesIO()
    generated_label.save(img_buffer, format="PNG")
    img_buffer.seek(0)

    st.sidebar.download_button(
        label="Download",
        data=img_buffer,
        file_name="AIEnergyScore.png",
        mime="image/png"
    )

if __name__ == "__main__":
    main()