Create app.py

app.py

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+import chainlit as cl
+import google.generativeai as genai
+import os
+
+# 🔑 Configure Gemini API
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+
+# 🧠 Simple memory per user
+memory = {}
+
+# 🎭 Role tone presets
+ROLES = {
+    "customer": "You are NovaTech’s kind and empathetic customer support agent.",
+    "employee": "You are NovaTech’s helpful internal assistant for company staff.",
+    "manager": "You are NovaTech’s smart business insights assistant. Be concise and professional."
+}
+
+@cl.on_chat_start
+async def start():
+    await cl.Message(
+        content="👋 Welcome to **NovaTech Solutions Virtual Assistant!**\nPlease tell me your role: customer, employee, or manager."
+    ).send()
+
+@cl.on_message
+async def main(message: cl.Message):
+    user = message.author or "guest"
+    user_mem = memory.get(user, {"role": "customer", "history": ""})
+
+    text = message.content.strip().lower()
+
+    # Role detection / setting
+    if text in ROLES:
+        user_mem["role"] = text
+        memory[user] = user_mem
+        await cl.Message(
+            content=f"✅ Got it! You are a **{text}**. How can I assist you today?"
+        ).send()
+        return
+
+    # Build prompt
+    prompt = f"""{ROLES[user_mem['role']]}
+Previous chat: {user_mem['history']}
+User: {message.content}
+Reply as NovaTech’s AI assistant with professionalism and empathy."""
+
+    model = genai.GenerativeModel("gemini-1.5-flash")
+    response = model.generate_content(prompt)
+    reply = response.text
+
+    # Update memory
+    user_mem["history"] += f"User: {message.content}\nAI: {reply}\n"
+    memory[user] = user_mem
+
+    await cl.Message(content=reply).send()