Update journal_utils.py

journal_utils.py

@@ -3,6 +3,10 @@ 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):
@@ -57,25 +61,86 @@ def get_weekly_summary():
         "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
+    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.savefig("assets/emotion_map.png")
-    return "assets/emotion_map.png"
+    plt.xlabel("Mood")
+    plt.ylabel("Frequency")
+    plt.tight_layout()
+
+    save_path = "assets/emotion_map.png"
+    plt.savefig(save_path)
+    plt.close()
+    return save_path
 
-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 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 "\n".join([f"{k}: {v}" for k, v in insights.items()])