app.py

import gradio as gr
from google import generativeai as genai  # Gemini GenAI SDK :contentReference[oaicite:0]{index=0}
import os

# — Load and configure Gemini API key from HF Secrets
gemini_api_key = os.getenv("GEMINI_API_KEY")
if not gemini_api_key:
    raise ValueError("GEMINI_API_KEY not set in environment")  # secure secret 
genai.configure(api_key=gemini_api_key)

# — Path to your uploaded business.txt in the Space
business_file = os.path.join(os.path.dirname(__file__), "business.txt")

def chat_with_business(message, history):
    # 1️⃣ Read the business knowledge
    with open(business_file, "r", encoding="utf-8") as f:
        business_info = f.read().strip()

    # 2️⃣ Build the system prompt
    system_prompt = (
        "You are a helpful customer-care assistant. "
        "Use only the information below to answer questions. "
        "If the answer is not present, reply 'Yeh information abhi available nahi hai.'\n\n"
        f"{business_info}\n\n"
    )

    # 3️⃣ Call Gemini 2.5 Flash to generate response :contentReference[oaicite:1]{index=1}
    model = genai.GenerativeModel(model_name="gemini-2.5-flash-preview-04-17")
    response = model.generate_content(
        system_prompt + "User: " + message
    )

    # 4️⃣ Return the assistant’s reply
    return response.text

# — Build Gradio frontend (Blocks API for future customization)
with gr.Blocks(theme="soft") as demo:
    gr.Markdown("## 🌿 My Business Bot")
    gr.Markdown("*Ask anything about your business in Hindi-English*")
    chatbot = gr.Chatbot(elem_id="chatbox", height=400)
    user_input = gr.Textbox(placeholder="Type your question here...", show_label=False)

    user_input.submit(
        lambda msg, hist: (chat_with_business(msg, hist), ""), 
        [user_input, chatbot], 
        [chatbot, user_input]
    )

if __name__ == "__main__":
    demo.launch()