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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer, pipeline
import gradio as gr
# Load the multilingual translation model (supports Tamil <-> English)
model_name = "facebook/m2m100_418M"
tokenizer = M2M100Tokenizer.from_pretrained(model_name)
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
# Load text generation pipeline (you can replace "gpt2" with your preferred model)
text_generator = pipeline("text-generation", model="gpt2")
def translate_tamil_to_english(text):
if not text.strip():
return ""
tokenizer.src_lang = "ta"
encoded = tokenizer(text, return_tensors="pt")
generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.get_lang_id("en"))
return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
def process_text(tamil_text):
if not tamil_text.strip():
return "Please enter Tamil text.", None, ""
try:
# Step 1: Translate Tamil to English
translation = translate_tamil_to_english(tamil_text)
except Exception as e:
return f"Translation error: {str(e)}", None, ""
try:
# Step 2: Generate English text based on translation
generated = text_generator(translation, max_length=50, num_return_sequences=1)
generated_text = generated[0]['generated_text']
except Exception as e:
return translation, None, f"Text generation error: {str(e)}"
# Step 3: Placeholder for image generation
# Replace with your own image generation model/function
image_url = "https://via.placeholder.com/512.png?text=Generated+Image"
# Step 4: Description based on generated text (you can use an image captioning model here)
description = f"Generated description: {generated_text}"
return translation, image_url, description
iface = gr.Interface(
fn=process_text,
inputs=gr.Textbox(lines=2, label="Enter Tamil text"),
outputs=[
gr.Textbox(label="Translated English Text"),
gr.Image(label="Generated Image"),
gr.Textbox(label="Image Description"),
],
title="Tamil to English Translation + Text & Image Generation",
description="Enter Tamil text, get English translation, generated text, image, and description."
)
if __name__ == "__main__":
iface.launch()
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