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from transformers import pipeline
import gradio as gr
# from diffusers import DiffusionPipeline
# from diffusers import FluxPipeline
import torch
import time
import requests
import io
import os
from PIL import Image
# Load models
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-dra-en")
# summarizer = pipeline("summarization", model="Falconsai/text_summarization")
# image_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16).to("cpu")
# for summarizer api
SUMMARIZER_API_URL = "https://api.groq.com/openai/v1/chat/completions"
summarizer_headers = {"Authorization": f"Bearer {os.getenv('GROQ_API_TOKEN')}",
"Content-Type": "application/json"}
# for image api
IMAGE_API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
img_headers = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}
def query(payload):
response = requests.post(IMAGE_API_URL, headers=img_headers, json=payload)
return response.content
# Translate Tamil to English
def translate_tamil_to_english(text):
time.sleep(2)
result = translator(text)
return result[0]['translation_text']
# Summarize English Paragraph
def summarize_english_text(paragraph):
time.sleep(2)
# Request payload
payload = {
"model": "mixtral-8x7b-32768",
"messages": [
{"role": "system", "content": "Create a summary of below paragraph in 30 words max"},
{"role": "user", "content": paragraph}
],
"max_tokens": 100 # number of words in the output.
}
# Send POST request to Groq API
response = requests.post(SUMMARIZER_API_URL, json=payload, headers=summarizer_headers)
# Check if the request was successful
if response.status_code == 200:
# Parse the JSON response
result = response.json()
# Extract and print the generated text
generated_text = result['choices'][0]['message']['content']
return generated_text
else:
return f"Error: {response.status_code}, {response.text}"
# Generate image from English text
def english_text_to_image(prompt):
image_bytes = query({
"inputs": prompt,
})
image = Image.open(io.BytesIO(image_bytes))
return image
with gr.Blocks() as app:
gr.Markdown("# Multifunctional Gradio App")
with gr.Row():
tamil_input = gr.Textbox(lines=2, placeholder="Enter Tamil text here...")
english_output = gr.Textbox(label="English Translation")
translate_button = gr.Button("Translate")
with gr.Row():
translate_button.click(translate_tamil_to_english, inputs=tamil_input, outputs=english_output)
with gr.Row():
english_paragraph_input = gr.Textbox(lines=5, placeholder="Enter English paragraph here...")
summary_output = gr.Textbox(label="Summary")
summarize_button = gr.Button("Summarize")
summarize_button.click(summarize_english_text, inputs=english_paragraph_input, outputs=summary_output)
with gr.Row():
english_text_input = gr.Textbox(lines=2, placeholder="Enter English text for image generation...")
image_output = gr.Image(label="Generated Image")
generate_button = gr.Button("Generate Image", size="sm") # Make the button smaller
generate_button.click(english_text_to_image, inputs=english_text_input, outputs=image_output)
app.launch()
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