import gradio as gr
from transformers import pipeline

# Load models
gen_model = pipeline("text2text-generation", model="google/flan-t5-large")
translator_en_ar = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")  # English to Arabic
translator_ar_en = pipeline("translation", model="Helsinki-NLP/opus-mt-ar-en")  # Arabic to English
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")

def get_plant_info(plant_name, language):
    if language == "English":
        prompt = (
            f"Provide detailed information about {plant_name}. "
            f"Include its scientific name, growing conditions (light, water, soil type), "
            f"common uses, and how to take care of it."
        )
        response = gen_model(prompt, min_length=50, max_length=300)[0]["generated_text"]
    else:  # Arabic
        translated_name = translator_ar_en(plant_name)[0]["translation_text"]  # Convert Arabic input to English
        prompt = (
            f"Provide detailed information about {translated_name}. "
            f"Include its scientific name, growing conditions (light, water, soil type), "
            f"common uses, and how to take care of it."
        )
        response_en = gen_model(prompt, min_length=50, max_length=300)[0]["generated_text"]
        response = translator_en_ar(response_en)[0]["translation_text"]  # Convert English output back to Arabic

    return response

# Gradio UI
interface = gr.Interface(
    fn=get_plant_info,
    inputs=[
        gr.Textbox(label="Enter Plant Name / أدخل اسم النبات"),
        gr.Radio(["English", "العربية"], label="Choose Language / اختر اللغة")
    ],
    outputs=gr.Textbox(label="Plant Information / معلومات النبات", lines=10),
    title="Plant Information App",
    description="Enter a plant name and select a language to get detailed information."
)

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