Update journal_utils.py

journal_utils.py

@@ -1,73 +1,81 @@
-import json, os
-import markdown2
-import matplotlib.pyplot as plt
-from fpdf import FPDF
+import os, json
 from datetime import datetime
 from collections import Counter
+from fpdf import FPDF
+import matplotlib.pyplot as plt
 
-LOG_FILE = "journal_log.json"
-
-def log_entry(data):
-    if os.path.exists(LOG_FILE):
-        with open(LOG_FILE, "r") as f:
-            logs = json.load(f)
-    else:
-        logs = []
-
+# --- 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": data["mood"],
-        "mode": data["mode"],
-        "entry": data["entry"],
-        "response": data["response"]
+        "mood": mood,
+        "user": user_msg,
+        "ai": ai_reply
     })
+    with open(file, "w") as f: json.dump(logs, f, indent=2)
 
-    with open(LOG_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)
 
-def export_to_pdf(data, filename="journal_export.pdf"):
+# --- Export ---
+def export_to_pdf(entry, mood, mode):
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
-    for k, v in data.items():
-        pdf.multi_cell(0, 10, f"{k.upper()}:\n{v}\n", align="L")
-    pdf.output(filename)
-    return filename
+    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(data, filename="journal_export.md"):
-    md_content = ""
-    for k, v in data.items():
-        md_content += f"### {k}\n{v}\n\n"
-    with open(filename, "w") as f:
-        f.write(md_content)
-    return filename
+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)
+    }
 
 def generate_emotion_map():
-    if not os.path.exists(LOG_FILE): return None
-    with open(LOG_FILE, "r") as f:
+    if not os.path.exists("journal_log.json"): return None
+    with open("journal_log.json") as f:
         logs = json.load(f)
+    moods = [x["mood"] for x in logs]
+    mood_count = Counter(moods)
+    plt.bar(mood_count.keys(), mood_count.values())
+    plt.title("Mood Trend")
+    plt.savefig("assets/emotion_map.png")
+    return "assets/emotion_map.png"
 
-    mood_counts = Counter([log["mood"] for log in logs])
-    moods = list(mood_counts.keys())
-    counts = list(mood_counts.values())
-
-    plt.figure(figsize=(6,4))
-    plt.bar(moods, counts)
-    plt.title("Weekly Mood Map")
-    plt.xlabel("Mood")
-    plt.ylabel("Frequency")
-    plt.savefig("emotion_map.png")
-    return "emotion_map.png"
-
-def get_weekly_summary():
-    if not os.path.exists(LOG_FILE): return "No data available."
-    with open(LOG_FILE, "r") as f:
-        logs = json.load(f)[-7:]  # Last 7 entries
-
+def get_counselor_view():
+    if not os.path.exists("journal_log.json"): return "No logs yet."
+    with open("journal_log.json") as f:
+        logs = json.load(f)
     insights = {
-        "Total Entries": len(logs),
-        "Mood Distribution": Counter([log["mood"] for log in logs]),
-        "Frequent Words": Counter(" ".join([log["entry"] for log in logs]).split()).most_common(5)
+        "Total Journals": len(logs),
+        "Mood Counts": Counter(x["mood"] for x in logs),
+        "Flags (e.g., 'hopeless')": sum("hopeless" in x["entry"].lower() for x in logs),
+        "Flags (e.g., 'tired')": sum("tired" in x["entry"].lower() for x in logs)
     }
-
-    return insights
+    return "\n".join([f"{k}: {v}" for k, v in insights.items()])