app.py

import os
from dotenv import load_dotenv
import gradio as gr
from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
from agents.run import RunConfig
import asyncio

# Load environment variables from .env file
load_dotenv()

# Retrieve the Gemini API key from environment variables
gemini_api_key = os.getenv("GEMINI_API_KEY")

# Check if the API key is present; if not, raise an error
if not gemini_api_key:
    raise ValueError("GEMINI_API_KEY is not set. Please ensure it is defined in your .env file.")

# Configure the AsyncOpenAI client for Gemini API
external_client = AsyncOpenAI(
    api_key=gemini_api_key,
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
)

# Define the model
model = OpenAIChatCompletionsModel(
    model="gemini-2.0-flash",
    openai_client=external_client
)

# Configure the run settings
config = RunConfig(
    model=model,
    model_provider=external_client,
    tracing_disabled=True
)

# Define the translation function
async def translate_urdu_to_english(urdu_text):
    agent = Agent(
        name="Translator",
        instructions="You are a Translator, always translate Urdu sentences into English Language.",
        model=model
    )
    result = await Runner.run(agent, urdu_text, run_config=config)
    return result.final_output

# Gradio interface function
def gradio_translate(urdu_input):
    # Run the async translation function within Gradio
    return asyncio.run(translate_urdu_to_english(urdu_input))

# Create Gradio interface
iface = gr.Interface(
    fn=gradio_translate,
    inputs=gr.Textbox(label="Urdu Input", placeholder="Enter Urdu sentence here..."),
    outputs=gr.Textbox(label="English Translation"),
    title="Urdu to English Translator",
    description="Enter an Urdu sentence to get its English translation using the Gemini API."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()