dashboard.py

# dashboard.py

import firebase_admin
from firebase_admin import credentials, firestore
import pandas as pd
import matplotlib.pyplot as plt
import io
import gradio as gr

# Initialize Firebase Admin SDK (ensure the JSON file is in your space)
if not firebase_admin._apps:
    cred = credentials.Certificate("evotransformer-firebase-adminsdk-fbsvc-37a4b838aa.json")
    firebase_admin.initialize_app(cred)

db = firestore.client()

def fetch_logs():
    docs = db.collection("evo_feedback_logs").stream()
    data = []
    for doc in docs:
        d = doc.to_dict()
        if all(k in d for k in ["goal", "evo", "gpt", "correct"]):
            data.append(d)
    return pd.DataFrame(data)

def generate_dashboard():
    df = fetch_logs()
    if df.empty:
        return "No data yet.", None

    df["evo_correct"] = df["evo"] == df["correct"]
    df["gpt_correct"] = df["gpt"] == df["correct"]

    evo_acc = df["evo_correct"].mean()
    gpt_acc = df["gpt_correct"].mean()
    agreement = (df["evo"] == df["gpt"]).mean()

    fig, ax = plt.subplots(figsize=(6, 4))
    counts = [
        df["evo_correct"].sum(),
        df["evo_correct"].count() - df["evo_correct"].sum(),
        df["gpt_correct"].sum(),
        df["gpt_correct"].count() - df["gpt_correct"].sum()
    ]
    bars = ax.bar(["Evo Correct", "Evo Wrong", "GPT Correct", "GPT Wrong"], counts)
    ax.set_title("Model Accuracy Summary")
    ax.set_ylabel("Count")

    buf = io.BytesIO()
    plt.tight_layout()
    plt.savefig(buf, format="png")
    plt.close(fig)
    buf.seek(0)

    summary = (
        f"🧠 Evo Accuracy: {evo_acc:.2%}\n"
        f"🤖 GPT-3.5 Accuracy: {gpt_acc:.2%}\n"
        f"⚖️ Agreement Rate: {agreement:.2%}\n"
        f"📝 Total Examples Logged: {len(df)}"
    )

    return summary, buf

with gr.Blocks() as dashboard:
    gr.Markdown("## 📊 EvoTransformer Live Evolution Dashboard")
    gr.Markdown("> 🔄 *Below metrics are based on real-time user feedback.*")
    
    summary_text = gr.Textbox(label="Summary", interactive=False)
    plot = gr.Image(type="pil", label="Accuracy Plot")

    refresh = gr.Button("🔁 Refresh Data")
    refresh.click(fn=generate_dashboard, outputs=[summary_text, plot])

dashboard.launch()