Update agents.py

agents.py

@@ -1,100 +1,81 @@
-import os, json, requests
-
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-LLAMA_MODEL = "llama3-8b-8192"
-SLM_MODEL = "slm-1b"
-
-def call_groq(prompt, model=LLAMA_MODEL):
-    response = requests.post(
-        "https://api.groq.com/openai/v1/chat/completions",
-        headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
-        json={
-            "model": model,
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.7
-        }
-    )
-    if response.status_code != 200:
-        return f"[Groq API Error {response.status_code}]: {response.text}"
-    data = response.json()
-    return data.get("choices", [{}])[0].get("message", {}).get("content", "[No valid response]")
-
-# --- Empathetic Chat Agent ---
-def run_first_aider(message, mood):
-    prompt = f"""
-You're a warm and respectful AI listener. Respond to this user's message kindly and briefly, in 1-2 sentences.
-Do not give advice. Use supportive, non-clinical tone.
-
-Mood: {mood}
-Message: "{message}"
-"""
-    return call_groq(prompt, model=SLM_MODEL)
-
-# --- Introspect Agent (with chat/journal memory) ---
-def get_user_context():
-    context = ""
-    if os.path.exists("chat_log.json"):
-        with open("chat_log.json") as f:
-            chats = json.load(f)[-3:]
-            context += "\nRecent Conversations:\n"
-            for c in chats:
-                context += f"User: {c['user']}\nAI: {c['ai']}\n"
-    if os.path.exists("journal_log.json"):
-        with open("journal_log.json") as f:
-            logs = json.load(f)[-2:]
-            context += "\nJournal Entries:\n"
-            for j in logs:
-                context += f"{j['entry']} → {j['response']}\n"
-    return context if context.strip() else None
-
-def run_introspect(message, mood):
-    context = get_user_context()
-    if not context:
-        return "Let's first talk a bit or write a few journal entries."
-
-    prompt = f"""
-Based on this context:
-{context}
-
-The user says: "{message}"
-Their mood is: {mood}
-
-Offer a kind and short reflection. Suggest something gentle like a shift in perspective, or a small action.
-Never use words like therapy, CBT, mental health, or psychology.
-"""
-    return call_groq(prompt, model=LLAMA_MODEL)
+import os, json
+from datetime import datetime
+from collections import Counter
+from fpdf import FPDF
+import matplotlib.pyplot as plt
+
+# --- 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)
+    }
 
-# --- Journal Agent with mood context ---
-def get_mood_context():
-    if not os.path.exists("journal_log.json"): return []
+def generate_emotion_map():
+    if not os.path.exists("journal_log.json"): return None
     with open("journal_log.json") as f:
         logs = json.load(f)
-        return [x["mood"] for x in logs[-3:]]
-
-def run_journaling_pipeline(mood, entry, mode):
-    recent_moods = get_mood_context()
-    context_line = f"Recent moods: {', '.join(recent_moods)}." if recent_moods else "No recent mood history."
-    question = "What emotional patterns do you notice in your life recently?" if recent_moods else "What stood out to you emotionally today?"
-
-    prompt = f"""
-You are a calm, non-judgmental journaling assistant.
-
-User mode: {mode}
-Mood: {mood}
-User wrote: "{entry}"
-{context_line}
-Question: {question}
-
-Reflect back to the user with kind interpretation, help them notice something they may have missed.
-End with a gentle affirmation or reflective nudge.
-
-Avoid therapy talk.
-"""
-
-    response = call_groq(prompt, model=LLAMA_MODEL)
-    return {
-        "entry": entry,
-        "mood": mood,
-        "mode": mode,
-        "response": response
+    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"
+
+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()])