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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
if not firebase_admin._apps:
cred = credentials.Certificate("firebase_key.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
def render_dashboard():
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])
return dashboard
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