Update agents.py

agents.py

@@ -1,5 +1,4 @@
-import requests
-import os
+import os, json, requests
 
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 LLAMA_MODEL = "llama3-8b-8192"
@@ -15,49 +14,87 @@ def call_groq(prompt, model=LLAMA_MODEL):
             "temperature": 0.7
         }
     )
-    print(response)
-    return response.json()["choices"][0]["message"]["content"]
+    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]")
 
-def get_prompt(entry, mood, mode):
-    if "Workplace" in mode:
-        return f"""
-You are a workplace CBT/NLP counselor. Analyze this journal:
-
-"{entry}"
+# --- 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."
 
-Return:
-- Summary
-- Workplace Stressors
-- Cognitive Distortions
-- Reframe
-- Affirmation
+    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.
 """
-    else:
-        return f"""
-You are a CBT/NLP journaling agent. Analyze the following:
+    return call_groq(prompt, model=LLAMA_MODEL)
 
-"{entry}"
+# --- Journal Agent with mood context ---
+def get_mood_context():
+    if not os.path.exists("journal_log.json"): return []
+    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.
 
-Return:
-- Summary
-- Emotional Triggers
-- Distortions
-- CBT Reframe
-- Daily Affirmation
+Avoid therapy talk.
 """
 
-def run_journaling_pipeline(mood, entry, mode):
-    if not entry.strip():
-        return {"error": "Empty entry."}
-    prompt = get_prompt(entry, mood, mode)
-    output = call_groq(prompt)
+    response = call_groq(prompt, model=LLAMA_MODEL)
     return {
+        "entry": entry,
         "mood": mood,
         "mode": mode,
-        "entry": entry,
-        "response": output
+        "response": response
     }