dashboard.py

import matplotlib.pyplot as plt
import firebase_admin
from firebase_admin import credentials, firestore
import io
from PIL import Image
import os
import json

# Initialize Firebase if not already initialized
if not firebase_admin._apps:
    cred = credentials.Certificate("firebase_key.json")
    firebase_admin.initialize_app(cred)

db = firestore.client()

# ✅ Feedback Summary Bar Chart
def update_dashboard_plot():
    logs_ref = db.collection("evo_feedback")
    docs = logs_ref.stream()

    count_1 = 0
    count_2 = 0
    for doc in docs:
        data = doc.to_dict()
        winner = data.get("winner", "")
        if winner == "1":
            count_1 += 1
        elif winner == "2":
            count_2 += 1

    # Generate a bar chart
    fig, ax = plt.subplots()
    ax.bar(["Solution 1", "Solution 2"], [count_1, count_2], color=["blue", "green"])
    ax.set_ylabel("Votes")
    ax.set_title("🗳️ EvoTransformer Feedback Summary")
    plt.tight_layout()

    # Convert matplotlib figure to PIL Image
    buf = io.BytesIO()
    plt.savefig(buf, format="png")
    buf.seek(0)
    img = Image.open(buf)
    return img

# ✅ Accuracy vs Generation with Architecture Tooltips
def evolution_accuracy_plot():
    try:
        log_path = "trained_model/evolution_log.json"
        if not os.path.exists(log_path):
            fig, ax = plt.subplots()
            ax.text(0.5, 0.5, "No evolution log found", ha="center", va="center")
            plt.tight_layout()
            buf = io.BytesIO()
            plt.savefig(buf, format="png")
            buf.seek(0)
            return Image.open(buf)

        with open(log_path, "r") as f:
            log_data = json.load(f)

        if not log_data:
            fig, ax = plt.subplots()
            ax.text(0.5, 0.5, "Evolution log is empty", ha="center", va="center")
            plt.tight_layout()
            buf = io.BytesIO()
            plt.savefig(buf, format="png")
            buf.seek(0)
            return Image.open(buf)

        generations = list(range(1, len(log_data) + 1))
        accuracies = [round(entry.get("accuracy", 0), 4) for entry in log_data]
        tooltips = [
            f"L: {entry.get('num_layers', '?')}, H: {entry.get('num_heads', '?')}, FFN: {entry.get('ffn_dim', '?')}, Mem: {entry.get('use_memory', '?')}"
            for entry in log_data
        ]

        fig, ax = plt.subplots(figsize=(6, 4))
        ax.plot(generations, accuracies, marker='o', label="Accuracy", color="purple")

        for i, tooltip in enumerate(tooltips):
            ax.annotate(tooltip, (generations[i], accuracies[i]), fontsize=7, xytext=(0, 6), textcoords='offset points')

        ax.set_xlabel("Generation")
        ax.set_ylabel("Accuracy")
        ax.set_title("📈 EvoTransformer Evolution Accuracy")
        ax.set_ylim([0, 1.05])
        ax.grid(True)
        plt.tight_layout()

        buf = io.BytesIO()
        plt.savefig(buf, format="png")
        buf.seek(0)
        return Image.open(buf)

    except Exception as e:
        fig, ax = plt.subplots()
        ax.text(0.5, 0.5, f"Error: {e}", ha="center", va="center")
        plt.tight_layout()
        buf = io.BytesIO()
        plt.savefig(buf, format="png")
        buf.seek(0)
        return Image.open(buf)