app.py

import gradio as gr
import asyncio
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_google_genai import ChatGoogleGenerativeAI
import os
import json

creds_json = os.getenv("GOOGLE_APPLICATION_CREDENTIALS_JSON")

# Write it to a temporary file
with open("cred.json", "w") as f:
    f.write(creds_json)

# Point the environment variable to it
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "cred.json"

GKEY = os.getenv("GAPI_KEY")
llm = ChatGoogleGenerativeAI(
    model = "gemini-1.5-flash",
    GOOGLE_API_KEY = GKEY,
    )

PROMPTS = {
    "ceo": "You are a greedy CEO. Your main goal is maximizing company profit while keeping costs low. Given this product: {input}, what is the best marketing strategy to achieve that?",
    "marketing_intern": "You are a Marketing Intern. Your ideas are sometimes genius, sometimes dumb, and sometimes cliché. Given this product: {input}, suggest creative marketing ideas.",
    "marketing_strategist": "You are a Marketing Strategist. Given this product: {input} and the ideas from the Marketing Intern, analyze the pros and cons.",
}

def run_agent(role, input_text):
    """Run a specific agent based on its role."""
    messages = [
        SystemMessage(content=PROMPTS[role].format(input=input_text)),
        HumanMessage(content=input_text),
    ]
    response = llm(messages)
    return response.content

async def marketing_conversation(product_description):
    """Run a timed multi-agent marketing conversation."""

    last_response = product_description
    conversation = []

    ceo_output = run_agent("ceo", last_response)
    intern_output = run_agent("marketing_intern", ceo_output)
    strategist_output = run_agent("marketing_strategist", intern_output)

    response = (
        f"\n 🤑 *CEO:* {ceo_output}\n"
        f"\n 🎨 *Marketing Intern:* {intern_output}\n"
        f"\n 📊 *Marketing Strategist:* {strategist_output}\n"
        "---"
    )

    conversation.append(response)
    last_response = strategist_output

    return "\n".join(conversation)

def start_conversation(product_description):
    return asyncio.run(marketing_conversation(product_description))

# Create Gradio Interface
gr.Interface(
    fn=start_conversation,
    inputs=gr.Textbox(label="Enter Product Description"),
    outputs=gr.Textbox(label="Marketing Discussion"),
    title="Marketing Strategy Discussion",
    description="A CEO, Marketing Intern, and Marketing Strategist discuss how to market a product.",
).launch(share=True)