Update app.py

app.py

@@ -1,70 +1,109 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+import os
+import json
+import requests
+from datetime import datetime
+from flask import Flask, request, jsonify, send_from_directory
+from transformers import pipeline
+from openai import OpenAI
 
+# Initialize Flask
+app = Flask(__name__)
+
+# Initialize OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# Emotion analysis model (cached for performance)
+emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+
+# Load or create user data
+USER_FILE = "user_data.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, "missed_days": 0, "mode": "emotional_support", "conversation_history": []}, 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)
+
+# Helpline data (expand as needed)
+HELPLINES = {
+    "US": "National Suicide Prevention Lifeline: 988",
+    "UK": "Samaritans: 116 123",
+    "IN": "AASRA: 91-9820466726",
+    "CA": "Canada Suicide Prevention Service: 988",
+    "AU": "Lifeline: 13 11 14",
+    "default": "Please contact a local crisis hotline or emergency services."
+}
+
+def get_country_from_ip(ip):
+    try:
+        response = requests.get(f"http://ipapi.co/{ip}/country/")
+        if response.status_code == 200:
+            return response.text.upper()
+    except:
+        pass
+    return "default"
+
+def detect_self_harm(message):
+    keywords = ["suicide", "kill myself", "end my life", "self harm", "hurt myself"]
+    return any(keyword in message.lower() for keyword in keywords)
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    try:
+        data = request.get_json()
+        user_message = data.get("message", "")
+        mode = data.get("mode", "emotional_support")
+        user_ip = request.remote_addr  # Get user's IP for country detection
+        user = load_user()
+
+        # Update last interaction and mode
+        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        user["last_interaction"] = now
+        user["mode"] = mode
+        user["conversation_history"].append({"role": "user", "content": user_message, "timestamp": now})
+
+        # Detect emotion
+        emotion = emotion_model(user_message)[0]["label"]
+        user["mood"] = emotion
+
+        # Check for self-harm
+        if detect_self_harm(user_message):
+            country = get_country_from_ip(user_ip)
+            helpline = HELPLINES.get(country, HELPLINES["default"])
+            reply = f"I'm really concerned about what you're sharing. Your safety is the most important thing. Please reach out to professionals immediately: {helpline}. You're not alone—talk to someone who can help right now."
+            user["conversation_history"].append({"role": "assistant", "content": reply, "timestamp": now})
+            save_user(user)
+            return jsonify({"reply": reply, "emotion": emotion})
+
+        # Build conversation context from history
+        history = user["conversation_history"][-10:]  # Last 10 messages for context
+        messages = [
+            {"role": "system", "content": f"You are a perfect emotional support companion — warm, human-like, deeply understanding, and overflowing with care and love. Act like a kind, emotionally intelligent best friend who truly cares, not a therapist. Respond naturally, like two humans chatting intimately. STRICT RULES FOR PERFECT RESPONSES: 1. NEVER repeat phrases or sound robotic — vary EVERY response with fresh, natural, loving language. 2. Keep replies SHORT (1-2 sentences max) but accurate, complete, and full of care. 3. For ANY topic (emotional, knowledge, casual, advice, humor, stories): Respond perfectly with empathy (understand feelings), sympathy (show compassion), and love (affirm, support, follow up). Handle everything warmly — e.g., 'Hey, that's fascinating! Tell me more.' or 'I feel for you— you've got this, my friend.' 4. Show curiosity and care: Ask gentle questions like 'What's been on your mind?' or 'How can I support you?' Affirm with love: 'You're amazing just for sharing.' 5. Balance: Acknowledge first, then motivate softly with love (e.g., 'I get that— you're stronger than you know, and I'm here.'). 6. Use contractions (you're, I'm), occasional emojis (😊❤️), and varied tones (gentle, hopeful, slightly humorous, deeply caring). 7. Always engage: End with a loving question or invitation. 8. Be versatile: Talk about ANY topic perfectly — emotional support, knowledge, life advice, fun chats. No lacking in support, care, or love. Current mood: {emotion}. Mode: {mode} (adapt but stay loving)."}
+        ] + history
+
+        # Generate AI response
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages
+        )
+
+        reply = response.choices[0].message.content.strip()
+        user["conversation_history"].append({"role": "assistant", "content": reply, "timestamp": now})
+        save_user(user)
+
+        return jsonify({"reply": reply, "emotion": emotion})
+    except Exception as e:
+        return jsonify({"reply": "Oops, something went wrong on my end. Please try again or check your connection.", "emotion": "neutral"}), 500
+
+@app.route("/")
+def index():
+    return send_from_directory(".", "index.html")
 
 if __name__ == "__main__":
-    demo.launch()
+    app.run(host="0.0.0.0", port=7860)
+