journal_utils.py

import os, json
from datetime import datetime
from collections import Counter
from fpdf import FPDF
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
from collections import Counter


# --- Logging ---
def log_chat_interaction(user_msg, ai_reply, mood):
    file = "chat_log.json"
    logs = []
    if os.path.exists(file):
        with open(file) as f: logs = json.load(f)
    logs.append({
        "timestamp": datetime.utcnow().isoformat(),
        "mood": mood,
        "user": user_msg,
        "ai": ai_reply
    })
    with open(file, "w") as f: json.dump(logs, f, indent=2)

def log_entry(entry_obj):
    file = "journal_log.json"
    logs = []
    if os.path.exists(file):
        with open(file) as f: logs = json.load(f)
    logs.append({
        **entry_obj,
        "timestamp": datetime.utcnow().isoformat()
    })
    with open(file, "w") as f: json.dump(logs, f, indent=2)

# --- Export ---
def export_to_pdf(entry, mood, mode):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    pdf.multi_cell(0, 10, f"Journaling Mode: {mode}\nMood: {mood}\nEntry:\n{entry}")
    path = "journal_export.pdf"
    pdf.output(path)
    return path

def export_to_md(entry, mood, mode):
    text = f"### Journal Entry\n**Mode**: {mode}\n**Mood**: {mood}\n\n{entry}"
    path = "journal_export.md"
    with open(path, "w") as f: f.write(text)
    return path

# --- Counselor Analytics ---
def get_weekly_summary():
    if not os.path.exists("journal_log.json"):
        return {"error": "No logs yet."}
    with open("journal_log.json") as f:
        logs = json.load(f)[-7:]
    return {
        "total_entries": len(logs),
        "mood_trend": Counter(x["mood"] for x in logs),
        "frequent_words": Counter(" ".join(x["entry"] for x in logs).split()).most_common(7)
    }


from collections import Counter
import json
import os

def get_counselor_view():
    journal_entries = []
    chat_entries = []

    if os.path.exists("journal_log.json"):
        with open("journal_log.json") as f:
            journal_entries = json.load(f)

    if os.path.exists("chat_log.json"):
        with open("chat_log.json") as f:
            chat_entries = json.load(f)

    combined_texts = []
    for j in journal_entries:
        combined_texts.append(j["entry"])
        combined_texts.append(j["response"])

    for c in chat_entries:
        combined_texts.append(c["user"])
        combined_texts.append(c["ai"])

    all_text = " ".join(combined_texts).lower()

    keywords_of_concern = ["tired", "hopeless", "alone", "worthless", "angry", "burnout", "anxious"]
    flags = {kw: all_text.count(kw) for kw in keywords_of_concern}

    moods = [j["mood"] for j in journal_entries] + [c["mood"] for c in chat_entries]
    mood_counts = Counter(moods)

    return f"""
🧠 Counselor Dashboard Summary

📝 Total Journal Entries: {len(journal_entries)}
💬 Total Conversations: {len(chat_entries)}

📊 Mood Distribution:
{dict(mood_counts)}

⚠️ Flagged Terms Detected:
{flags}

🧩 Top Observations:
- Watch for spikes in fatigue, hopelessness, or anger.
- Consistent use of certain terms may suggest emotional load.
- Consider encouraging journaling, breathing, or referring to a professional if >3 flags occur.

"""


import os
import matplotlib.pyplot as plt
from collections import Counter

def generate_emotion_map():
    if not os.path.exists("journal_log.json"):
        return None

    # ✅ Ensure the folder exists
    os.makedirs("assets", exist_ok=True)

    with open("journal_log.json") as f:
        logs = json.load(f)
    
    moods = [x["mood"] for x in logs]
    mood_count = Counter(moods)

    plt.figure(figsize=(6, 4))
    plt.bar(mood_count.keys(), mood_count.values())
    plt.title("Mood Trend")
    plt.xlabel("Mood")
    plt.ylabel("Frequency")
    plt.tight_layout()

    save_path = "assets/emotion_map.png"
    plt.savefig(save_path)
    plt.close()
    return save_path