Create app.py

app.py

@@ -0,0 +1,65 @@
+import os
+import json
+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
+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}, 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)
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    data = request.get_json()
+    user_message = data.get("message", "")
+    user = load_user()
+
+    # Update last interaction time
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    user["last_interaction"] = now
+    save_user(user)
+
+    # Detect emotion
+    emotion = emotion_model(user_message)[0]["label"]
+
+    # Generate AI response
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are an empathetic emotional support assistant. Be friendly, warm, and encouraging, but not a therapist."},
+            {"role": "user", "content": user_message}
+        ]
+    )
+
+    reply = response.choices[0].message.content.strip()
+
+    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)