Update it.

app.py

@@ -1,176 +1,124 @@
 import os
 import json
-import random
-from datetime import datetime, timedelta
+import datetime
 from flask import Flask, request, jsonify, send_from_directory
 from transformers import pipeline
-from openai import OpenAI
-import requests
+import random
 
 app = Flask(__name__)
 
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
-
-USER_FILE = "user_data.json"
-CHAT_LOG_FILE = "recent_chat.json"
-
-if not os.path.exists(USER_FILE):
-    with open(USER_FILE, "w") as f:
-        json.dump({
-            "name": None,
-            "age": None,
-            "mood": None,
-            "last_interaction": None,
-            "mood_history": []
-        }, f)
-
-if not os.path.exists(CHAT_LOG_FILE):
-    with open(CHAT_LOG_FILE, "w") as f:
-        json.dump([], f)
-
-def load_user():
-    with open(USER_FILE, "r") as f:
-        return json.load(f)
-
-def save_user(data):
-    with open(USER_FILE, "w") as f:
-        json.dump(data, f)
-
-def load_chat_log():
-    with open(CHAT_LOG_FILE, "r") as f:
-        return json.load(f)
-
-def save_chat_log(logs):
-    cutoff = datetime.now() - timedelta(days=15)
-    logs = [l for l in logs if datetime.strptime(l["time"], "%Y-%m-%d %H:%M:%S") > cutoff]
-    with open(CHAT_LOG_FILE, "w") as f:
-        json.dump(logs, f)
-
-def get_country():
-    try:
-        res = requests.get("https://ipapi.co/json/").json()
-        return res.get("country_name", "default").lower()
-    except:
-        return "default"
-
-HELPLINES = {
-    "united states": "If you are in the U.S., please contact the **988 Suicide and Crisis Lifeline** by dialing 988 (24/7).",
-    "united kingdom": "If you’re in the UK, contact **Samaritans** at 116 123 (24/7).",
-    "india": "If you’re in India, you can reach **AASRA** at 91-9820466726 or **Snehi** at 91-9582208181.",
-    "canada": "If you’re in Canada, dial or text **988** for the Talk Suicide Canada line (24/7).",
-    "australia": "If you’re in Australia, contact **Lifeline Australia** at 13 11 14 (24/7).",
-    "default": "If you’re in crisis, please reach out to your local emergency number or trusted mental health service. You are not alone 💛"
-}
-
-MOTIVATIONAL_QUOTES = [
-    "Even the darkest night will end and the sun will rise 🌅",
-    "You are stronger than you think 💪",
-    "It’s okay to rest. Healing takes time 🌿",
-    "Every storm runs out of rain ⛈️",
-    "Be kind to yourself — you’re doing your best 💛",
-    "Peace begins the moment you let things be 🌸",
-    "The fact that you’re trying means you haven’t given up 🌼",
+# ========= BASIC CONFIG =========
+USER_MEMORY_FILE = "user_memory.json"
+
+# Load lightweight emotion model (tiny)
+emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
+
+# ========== SIMPLE MEMORY ==========
+def load_memory():
+    if os.path.exists(USER_MEMORY_FILE):
+        with open(USER_MEMORY_FILE, "r") as f:
+            return json.load(f)
+    return {}
+
+def save_memory(memory):
+    with open(USER_MEMORY_FILE, "w") as f:
+        json.dump(memory, f)
+
+memory = load_memory()
+
+# ========= PREDEFINED RESPONSES =========
+supportive_openers = [
+    "That sounds tough, but I’m here for you.",
+    "It takes courage to talk about that.",
+    "I appreciate you sharing that with me.",
+    "You’re not alone in this, and I’m here to listen.",
+    "Thank you for trusting me with that.",
 ]
 
-CRISIS_KEYWORDS = [
-    "suicide", "kill myself", "end my life", "want to die", "can't go on", "life not worth"
+motivational_followups = [
+    "You’ve got more strength than you think.",
+    "Every step, no matter how small, is progress.",
+    "Healing takes time, and that’s perfectly okay.",
+    "Would you like to talk about what might help today?",
 ]
 
-@app.route("/chat", methods=["POST"])
+crisis_reply = (
+    "I’m really sorry that you’re feeling like this. "
+    "You’re not alone, and help is available. "
+    "If you are in immediate danger, please call your local emergency number or a trusted person. "
+    "If you're in India, you can contact AASRA Helpline at 91-9820466726. "
+    "In the U.S., call or text **988** for the Suicide and Crisis Lifeline."
+)
+
+# ========= HELPER FUNCTIONS =========
+def analyze_emotion(text):
+    try:
+        res = emotion_analyzer(text)
+        top_emotion = res[0][0]['label']
+        return top_emotion.lower()
+    except Exception:
+        return "neutral"
+
+def crisis_check(text):
+    crisis_keywords = ["suicide", "end my life", "kill myself", "can't go on", "hurt myself"]
+    return any(k in text.lower() for k in crisis_keywords)
+
+def get_supportive_response(user_name, text, emotion):
+    if crisis_check(text):
+        return crisis_reply
+
+    sympathy = random.choice(supportive_openers)
+    motivation = random.choice(motivational_followups)
+
+    if emotion in ["sadness", "fear", "anger"]:
+        return f"{sympathy} {motivation}"
+    elif emotion in ["joy", "love"]:
+        return f"That’s wonderful to hear, {user_name if user_name else 'friend'}! Keep that energy going."
+    else:
+        return f"I understand, {user_name if user_name else 'friend'}. Would you like to share a bit more?"
+
+# ========= ROUTES =========
+@app.route("/api/chat", methods=["POST"])
 def chat():
     data = request.get_json()
-    user_message = data.get("message", "").strip().lower()
-    user = load_user()
-    chat_log = load_chat_log()
-
-    # Crisis detection
-    if any(keyword in user_message for keyword in CRISIS_KEYWORDS):
-        country = get_country()
-        crisis_msg = (
-            "💛 I’m really concerned about how you’re feeling right now.\n"
-            "You don’t have to face this alone. "
-            + HELPLINES.get(country, HELPLINES["default"])
-            + "\n\nWould you like me to stay with you while you calm down a bit?"
-        )
-        return jsonify({"reply": crisis_msg, "emotion": "crisis"})
-
-    # Collect name
-    if not user["name"]:
-        user["name"] = user_message.capitalize()
-        save_user(user)
-        return jsonify({"reply": f"Nice to meet you, {user['name']} 🌼 Can you tell me your age?", "emotion": "calm"})
-
-    # Collect age
-    if not user["age"]:
-        try:
-            user["age"] = int(user_message)
-            save_user(user)
-            return jsonify({"reply": "Thank you 💛 Now tell me, how have you been feeling lately?", "emotion": "curious"})
-        except ValueError:
-            return jsonify({"reply": "Could you please tell me your age in numbers? 😊", "emotion": "neutral"})
-
-    # Follow-up if user felt bad last time
-    if user["last_interaction"]:
-        last_time = datetime.strptime(user["last_interaction"], "%Y-%m-%d %H:%M:%S")
-        if datetime.now() - last_time > timedelta(hours=12):
-            if user["mood"] in ["sadness", "anger", "fear"]:
-                follow_up = f"Hey {user['name']} 💛 You were feeling low last time we talked. How are you feeling now?"
-                user["last_interaction"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                save_user(user)
-                return jsonify({"reply": follow_up, "emotion": "warm"})
-
-    # Emotion detection
-    emotion_result = emotion_model(user_message)[0]
-    emotion = emotion_result["label"].lower()
-
-    # Update logs
-    user["mood"] = emotion
-    user["last_interaction"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    user["mood_history"].append({"emotion": emotion, "time": user["last_interaction"]})
-    save_user(user)
-    chat_log.append({
-        "message": user_message,
-        "emotion": emotion,
-        "time": user["last_interaction"]
-    })
-    save_chat_log(chat_log)
-
-    # Tone setup
-    tone_instruction = (
-        "You are Serenity, an emotionally intelligent AI companion. "
-        "Be warm, human-like, calm, and empathetic. Show that you care deeply and remember feelings. "
-        "Do not repeat 'I'm sorry to hear that'; instead vary your empathetic responses naturally. "
-        "Encourage reflection and hope. "
-    )
-
-    if user["age"] < 18:
-        tone_instruction += "Speak in a gentle, caring tone suited for teenagers."
-    else:
-        tone_instruction += "Speak with emotional maturity and calm wisdom."
-
-    ai_response = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": tone_instruction},
-            {"role": "user", "content": user_message},
-        ],
-    )
-    reply = ai_response.choices[0].message.content.strip()
-
-    # Add motivational quote if sad
-    if emotion in ["sadness", "fear", "anger"]:
-        reply += f"\n\n💬 *Motivational Thought:* {random.choice(MOTIVATIONAL_QUOTES)}"
+    user_message = data.get("message", "")
+    user_name = data.get("name", "friend")
+    user_age = data.get("age", None)
+
+    if not user_message:
+        return jsonify({"response": "Please say something so I can respond."})
+
+    # analyze mood
+    emotion = analyze_emotion(user_message)
 
-    # Mood trend summary
-    if len(user["mood_history"]) > 3:
-        recent = [m['emotion'] for m in user["mood_history"][-3:]]
-        if all(e in ["happiness", "joy", "calm"] for e in recent):
-            reply += "\n\n🌞 You’ve been calmer lately — I’m proud of your progress!"
-        elif all(e in ["sadness", "fear", "anger"] for e in recent):
-            reply += "\n\n🌧️ I’ve noticed some tough days lately — please remember you’re not alone."
+    # load & update memory
+    memory[user_name] = {
+        "last_emotion": emotion,
+        "last_message": user_message,
+        "timestamp": datetime.datetime.now().isoformat()
+    }
+    save_memory(memory)
 
-    return jsonify({"reply": reply, "emotion": emotion})
+    response_text = get_supportive_response(user_name, user_message, emotion)
+
+    return jsonify({"response": response_text, "emotion": emotion})
+
+
+@app.route("/api/check_mood", methods=["POST"])
+def check_mood():
+    data = request.get_json()
+    user_name = data.get("name", "")
+    if not user_name or user_name not in memory:
+        return jsonify({"message": "Hello! How are you feeling today?"})
+
+    last_entry = memory[user_name]
+    last_time = datetime.datetime.fromisoformat(last_entry["timestamp"])
+    days_since = (datetime.datetime.now() - last_time).days
+
+    if days_since <= 15 and last_entry["last_emotion"] in ["sadness", "fear", "anger"]:
+        return jsonify({"message": f"Hey {user_name}, you were feeling a bit down last time. How are you feeling today?"})
+    else:
+        return jsonify({"message": f"Welcome back, {user_name}! How’s everything going?"})
 
 
 @app.route("/")
@@ -179,4 +127,4 @@ def index():
 
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))