Update agents.py

agents.py

@@ -1,81 +1,131 @@
-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)
-    }
+import os
+import json
+import requests
 
-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)
-    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."
+# Set your API Key securely
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+LLAMA_MODEL = "llama3-8b-8192"
+SLM_MODEL = "slm-1b"
+
+# ---------------------------
+# Groq API Call Wrapper
+# ---------------------------
+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}"
+
+    try:
+        data = response.json()
+        return data.get("choices", [{}])[0].get("message", {}).get("content", "[No valid response]")
+    except Exception as e:
+        return f"[Groq Parsing Error]: {str(e)}"
+
+# ---------------------------
+# Empathetic First-Aider 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.
+- Never give advice.
+- Be supportive, clear, emotionally kind, and safe.
+- Maintain boundaries.
+
+User mood: {mood}
+Message: "{message}"
+"""
+    return call_groq(prompt, model=SLM_MODEL)
+
+# ---------------------------
+# Context-Aware Introspect Agent
+# ---------------------------
+def get_user_context():
+    context = ""
+
+    if os.path.exists("chat_log.json"):
+        with open("chat_log.json") as f:
+            chats = json.load(f)[-3:]  # last 3 chat messages
+            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"Mood: {j['mood']}\nEntry: {j['entry']}\nAI Response: {j['response']}\n"
+
+    return context.strip() if context.strip() else None
+
+def run_introspect(message, mood):
+    context = get_user_context()
+    if not context:
+        return "Let's talk a bit or journal first so I can help you reflect better."
+
+    prompt = f"""
+You're a calm, thoughtful AI helping a user gently reflect on their emotional patterns.
+
+Context from past chats and journals:
+{context}
+
+User's new message: "{message}"
+Mood: {mood}
+
+Instructions:
+- Highlight any potential recurring emotional themes (gently).
+- Suggest a new way to think about the situation (without naming therapy techniques).
+- End with a kind affirmation or journaling suggestion.
+
+Be kind, short, helpful, and never mention psychology, CBT, or NLP.
+"""
+    return call_groq(prompt, model=LLAMA_MODEL)
+
+# ---------------------------
+# Journaling Agent (Context-Aware, CBT/NLP Masked)
+# ---------------------------
+def get_mood_context():
+    if not os.path.exists("journal_log.json"):
+        return []
     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 [x["mood"] for x in logs[-3:]]
+
+def run_journaling_pipeline(mood, entry, mode):
+    recent_moods = get_mood_context()
+    mood_summary = f"Recent moods: {', '.join(recent_moods)}." if recent_moods else "No mood history."
+    reflective_prompt = "What patterns have you noticed in how you've been feeling lately?" if recent_moods else "What stood out to you emotionally today?"
+
+    prompt = f"""
+You're a reflective journaling assistant. Help the user explore their thoughts kindly and safely.
+
+User wrote:
+"{entry}"
+Journaling Mode: {mode}
+Mood: {mood}
+{mood_summary}
+Prompt: {reflective_prompt}
+
+Instructions:
+- Reflect what the user might be feeling or thinking.
+- Suggest gentle rephrasing or ways to understand the situation better.
+- End with a kind affirmation or open-ended journaling question.
+- Do not mention therapy, CBT, NLP, psychology, or diagnosis.
+
+Be gentle and warm.
+"""
+    response = call_groq(prompt, model=LLAMA_MODEL)
+    return {
+        "entry": entry,
+        "mood": mood,
+        "mode": mode,
+        "response": response
     }
-    return "\n".join([f"{k}: {v}" for k, v in insights.items()])