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import gradio as gr
from transformers import pipeline
# Load models
translation_pipeline = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en")
text_generation_pipeline = pipeline("text-generation", model="gpt2")
# Simulated image generation (replace with Hugging Face Diffusers or similar if needed)
def generate_image(prompt: str) -> str:
# You can integrate actual image generation here
return f"https://via.placeholder.com/512?text={prompt.replace(' ', '+')}"
# Main function
def multimodal_pipeline(tamil_text: str):
# Step 1: Translate Tamil to English
translated = translation_pipeline(tamil_text)[0]["translation_text"]
# Step 2: Generate English text
generated = text_generation_pipeline(translated, max_length=50, do_sample=True)[0]["generated_text"]
# Step 3: Generate Image (simulate)
image_url = generate_image(generated)
return translated, generated, image_url
# Gradio Interface
interface = gr.Interface(
fn=multimodal_pipeline,
inputs=gr.Textbox(label="Enter Tamil Text", placeholder="உங்கள் தமிழ் உரையை இங்கே உள்ளிடவும்"),
outputs=[
gr.Textbox(label="English Translation"),
gr.Textbox(label="Generated Prompt"),
gr.Image(label="Generated Image"),
],
title="Tamil to Image Multimodal App",
description="This app translates Tamil to English, generates a descriptive sentence, and creates an image based on it."
)
if __name__ == "__main__":
interface.launch()
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