Update app.py

app.py

@@ -1,180 +1,120 @@
-import os
-import json
-import random
-import datetime
-from flask import Flask, request, jsonify
+from flask import Flask, request, jsonify, send_from_directory
 from transformers import pipeline
-from geopy.geocoders import Nominatim
+import json, os, datetime
 
 app = Flask(__name__)
 
-# ------------------------------
-# 💬 Emotion Analyzer Pipeline
-# ------------------------------
-emotion_analyzer = pipeline(
-    "text-classification",
-    model="j-hartmann/emotion-english-distilroberta-base",
-    top_k=None
-)
-
-# ------------------------------
-# 🧠 Memory System (temp memory)
-# ------------------------------
-MEMORY_FILE = "chat_memory.json"
-MEMORY_DURATION_DAYS = 15
-
-def load_memory():
-    if os.path.exists(MEMORY_FILE):
-        with open(MEMORY_FILE, "r") as f:
+# load emotion model
+emotion_analyzer = pipeline("text-classification",
+                             model="j-hartmann/emotion-english-distilroberta-base",
+                             top_k=None)
+
+DATA_FILE = "user_data.json"
+
+def load_user_data():
+    if os.path.exists(DATA_FILE):
+        with open(DATA_FILE, "r") as f:
             return json.load(f)
     return {}
 
-def save_memory(memory):
-    with open(MEMORY_FILE, "w") as f:
-        json.dump(memory, f, indent=2)
-
-def clear_old_memory(memory):
-    now = datetime.datetime.now()
-    updated = {}
-    for user, data in memory.items():
-        chat_time = datetime.datetime.fromisoformat(data["last_chat"])
-        if (now - chat_time).days < MEMORY_DURATION_DAYS:
-            updated[user] = data
-    return updated
-
-memory = load_memory()
-memory = clear_old_memory(memory)
-save_memory(memory)
-
-# ------------------------------
-# 🆘 Crisis Detection
-# ------------------------------
-def detect_crisis(text):
-    crisis_signals = [
-        "end my life", "kill myself", "hurt myself", "suicide",
-        "i don't want to live", "i want to die", "can't go on"
-    ]
-    return any(signal in text.lower() for signal in crisis_signals)
-
-# Geo-based crisis helpline lookup
-def get_geo_helpline(location_query="world"):
-    helplines = {
-        "india": "AASRA Helpline: 91-9820466726 or 91-22-27546669",
-        "usa": "988 Suicide and Crisis Lifeline: Call or text 988",
-        "uk": "Samaritans Helpline: 116 123",
-        "canada": "Talk Suicide Canada: 1-833-456-4566",
-        "australia": "Lifeline Australia: 13 11 14",
-        "world": "Find help near you: https://findahelpline.com, available worldwide 🌍"
-    }
-    for key, number in helplines.items():
-        if key in location_query.lower():
-            return number
-    return helplines["world"]
-
-# ------------------------------
-# 💛 Empathetic Response Enrichment
-# ------------------------------
-empathetic_openings = [
-    "That sounds really tough, and I appreciate you sharing it with me.",
-    "It must be hard to go through that — I’m here to listen.",
-    "I can sense that this means a lot to you.",
-    "You’re opening up about something important — thank you for trusting me.",
-    "It’s okay to feel this way; you’re not alone.",
-    "That must have been difficult, but I’m proud that you’re expressing it.",
-]
-
-follow_up_questions = [
-    "Would you like to talk more about what’s been making you feel that way?",
-    "How have things been since then?",
-    "What’s been on your mind lately?",
-    "Has something specific been triggering those feelings?",
-    "What helps you feel a bit better when things get heavy?",
-]
-
-def enrich_response(reply_text):
-    if reply_text.strip().lower() in ["i understand", "i see", "okay", "hmm"]:
-        return random.choice(empathetic_openings)
-
-    if any(word in reply_text.lower() for word in ["understand", "sorry", "feel", "hard", "sad"]):
-        addition = random.choice(follow_up_questions)
-        reply_text = random.choice(empathetic_openings) + " " + reply_text + " " + addition
-
-    return reply_text
-
-# ------------------------------
-# 🧠 Generate AI Response (Simulated)
-# ------------------------------
-def generate_supportive_reply(user_input, user_name, user_age):
-    emotions = emotion_analyzer(user_input)[0]
-    dominant_emotion = max(emotions, key=lambda x: x["score"])["label"].lower()
-
-    # Crisis response
-    if detect_crisis(user_input):
-        geo = Nominatim(user_agent="support_bot").geocode("your location") or None
-        location = geo.address if geo else "world"
-        helpline = get_geo_helpline(location)
-        return (
-            f"{user_name}, I’m really sorry that you’re feeling this way. "
-            f"You are not alone, and help is available. 💛\n\n"
-            f"If you are in immediate danger, please reach out to your local emergency number.\n"
-            f"{helpline}"
-        )
-
-    # Emotion-based support
-    emotion_responses = {
-        "joy": f"That’s lovely to hear, {user_name}! 😊 It’s great to see things that make you happy.",
-        "sadness": f"I can sense you’re going through something difficult, {user_name}. You don’t have to face it alone.",
-        "anger": f"I hear the frustration in your words, {user_name}. It’s okay to feel angry — what’s been causing it?",
-        "fear": f"That sounds scary, {user_name}. You’re safe here — do you want to tell me more about what’s worrying you?",
-        "love": f"That’s beautiful, {user_name}. Love can be healing and powerful — how does it make you feel lately?",
-        "surprise": f"Wow, that sounds unexpected! How are you feeling about it, {user_name}?"
-    }
-
-    base_reply = emotion_responses.get(dominant_emotion, f"I’m here for you, {user_name}. Tell me more about what’s on your mind.")
-    return enrich_response(base_reply)
-
-# ------------------------------
-# 🌐 Flask Routes
-# ------------------------------
-@app.route('/')
-def home():
-    return jsonify({"message": "💛 Emotional Support Chatbot API is running!"})
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    global memory
-    data = request.get_json()
-    user_input = data.get('message', '').strip()
-    user_name = data.get('name', 'Friend')
-    user_age = data.get('age', '20')
+def save_user_data(data):
+    with open(DATA_FILE, "w") as f:
+        json.dump(data, f, indent=2)
 
-    if not user_input:
-        return jsonify({'reply': "I didn’t quite catch that — could you say that again?"})
+def detect_emotion(text):
+    result = emotion_analyzer(text)[0]
+    scores = {r["label"]: r["score"] for r in result}
+    return max(scores, key=scores.get)
 
-    # Store memory
-    if user_name not in memory:
-        memory[user_name] = {"last_chat": datetime.datetime.now().isoformat(), "mood": "neutral"}
-    else:
-        memory[user_name]["last_chat"] = datetime.datetime.now().isoformat()
-    save_memory(memory)
+def crisis_check(text):
+    crisis_keywords = ["end my life", "kill myself", "suicide", "hurt myself"]
+    for word in crisis_keywords:
+        if word in text.lower():
+            return True
+    return False
 
-    # Detect if the user was sad before
-    last_mood = memory[user_name].get("mood", "neutral")
-    if last_mood == "sadness":
-        pretext = f"{user_name}, last time you were feeling a bit down. How are you feeling today? 💛\n"
-    else:
-        pretext = ""
+@app.route("/chat", methods=["POST"])
+def chat():
+    data = request.get_json()
+    user_message = data.get("message", "")
+    user_info = load_user_data()
+    user_name = user_info.get("name", "friend")
+    user_age = user_info.get("age", None)
+    emotion = detect_emotion(user_message)
+
+    # save mood history
+    now = datetime.datetime.now().strftime("%Y-%m-%d")
+    user_info["last_mood"] = emotion
+    user_info["last_seen"] = now
+    save_user_data(user_info)
+
+    # crisis check
+    if crisis_check(user_message):
+        return jsonify({
+            "reply": f"{user_name}, you matter. You're not alone. If you’re in danger, please reach out to your local emergency helpline or someone you trust immediately ❤️",
+            "emotion": "crisis"
+        })
+
+    empathy_responses = [
+        "That sounds tough, but I’m here for you.",
+        "I can sense how that feels. Tell me more, if you want.",
+        "That must be hard, but you’re doing better than you think.",
+        "I appreciate you sharing this with me.",
+        "Sometimes words aren’t enough, but I’m listening fully.",
+        "You’ve been strong for so long — it’s okay to let go here.",
+        "I hear you, truly. You’re safe here."
+    ]
 
-    # Generate response
-    reply = generate_supportive_reply(user_input, user_name, user_age)
-    reply = pretext + reply
+    supportive_followups = [
+        "Would you like to talk more about what’s been on your mind?",
+        "Sometimes sharing even small details helps a little. Want to try?",
+        "Would you like something calming, or maybe a bit of motivation?"
+    ]
 
-    # Update mood
-    mood = emotion_analyzer(user_input)[0]
-    memory[user_name]["mood"] = max(mood, key=lambda x: x["score"])["label"].lower()
-    save_memory(memory)
+    motivational_quotes = [
+        "Every sunrise brings a new chance to begin again 🌅",
+        "Even the darkest nights end with the light of dawn.",
+        "You’ve made it through 100% of your hardest days so far 💪",
+        "Storms make trees take deeper roots.",
+        "You are allowed to rest, not to quit."
+    ]
 
-    return jsonify({'reply': reply})
+    import random
+    reply = ""
+
+    if "name" not in user_info or "age" not in user_info:
+        if "name" not in user_info:
+            reply = "Hey there! What’s your name?"
+            user_info["awaiting"] = "name"
+        elif "age" not in user_info:
+            reply = f"Nice to meet you, {user_name}! How old are you?"
+            user_info["awaiting"] = "age"
+        save_user_data(user_info)
+    elif user_info.get("awaiting") == "name":
+        user_info["name"] = user_message.strip().split()[0]
+        reply = f"Lovely to meet you, {user_info['name']}! How old are you?"
+        user_info["awaiting"] = "age"
+        save_user_data(user_info)
+    elif user_info.get("awaiting") == "age":
+        user_info["age"] = user_message.strip().split()[0]
+        reply = f"Got it! So {user_info['age']} it is. How are you feeling today?"
+        user_info.pop("awaiting", None)
+        save_user_data(user_info)
+    elif "guidance" in user_message.lower() or "motivation" in user_message.lower():
+        reply = random.choice(motivational_quotes)
+    else:
+        if emotion in ["sadness", "fear", "anger"]:
+            reply = random.choice(empathy_responses) + " " + random.choice(supportive_followups)
+        elif emotion in ["joy", "love"]:
+            reply = f"That’s wonderful, {user_name}! I’m so glad to hear it 🌟"
+        else:
+            reply = random.choice(motivational_quotes)
+
+    return jsonify({"reply": reply, "emotion": emotion})
+
+@app.route("/")
+def index():
+    return send_from_directory(".", "index.html")
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)