app.py

import os
from huggingface_hub import login
from transformers import MarianMTModel, MarianTokenizer, pipeline
import requests
import io
from PIL import Image
import gradio as gr

# Set Hugging Face API key
hf_token = os.getenv("HUGGINGFACE_API_KEY")
if hf_token is None:
    raise ValueError("Hugging Face API key not found in environment variables.")

# Login to Hugging Face
login(token=hf_token)

# Define language codes for around 10 languages
language_codes = {
    "French": "fr",
    "Spanish": "es",
    "German": "de",
    "Tamil": "ta",
    "Hindi": "hi",
    "Chinese": "zh",
    "Russian": "ru",
    "Japanese": "ja",
    "Korean": "ko",
    "Arabic": "ar",
    "Portuguese": "pt",
    "Italian": "it"
}

model_name = "Helsinki-NLP/opus-mt-mul-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translator = pipeline("translation", model=model, tokenizer=tokenizer)

# Function for translation
def translate_text(input_text, src_lang):
    try:
        src_prefix = f">>{src_lang}<< " + input_text
        translation = translator(src_prefix, max_length=40)
        translated_text = translation[0]['translation_text']
        return translated_text
    except Exception as e:
        return f"An error occurred: {str(e)}"

# API credentials and endpoint for FLUX
flux_API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
flux_headers = {"Authorization": f"Bearer {hf_token}"}

# Function to generate image based on prompt
def generate_image(prompt):
    try:
        response = requests.post(flux_API_URL, headers=flux_headers, json={"inputs": prompt})
        if response.status_code == 200:
            image_bytes = response.content
            image = Image.open(io.BytesIO(image_bytes))
            return image
        else:
            print(f"Failed to get image: Status code {response.status_code}")
            return None
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

# API setup for Mistral model
mistral_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
mistral_headers = {"Authorization": f"Bearer {hf_token}"}

def generate_creative_text(translated_text):
    try:
        response = requests.post(mistral_API_URL, headers=mistral_headers, json={"inputs": translated_text})
        if response.status_code == 200:
            creative_text = response.json()[0]['generated_text']
            return creative_text
        else:
            return "Error generating creative text"
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Main function to handle full workflow
def translate_generate_image_and_text(input_text, src_lang):
    # Step 1: Translate input text
    translated_text = translate_text(input_text, language_codes[src_lang])

    # Step 2: Generate an image
    image = generate_image(translated_text)

    # Step 3: Generate creative text based on the translation
    creative_text = generate_creative_text(translated_text)

    return translated_text, creative_text, image

# Gradio interface
interface = gr.Interface(
    fn=translate_generate_image_and_text,
    inputs=[
        gr.Textbox(label="Enter text for translation"),
        gr.Dropdown(choices=list(language_codes.keys()), label="Source Language")
    ],
    outputs=[
        gr.Textbox(label="Translated Text"),
        gr.Textbox(label="Creative Text"),
        gr.Image(label="Generated Image")
    ],
    title="Multilingual Translation, Image, and Creative Text Generator",
    description="Translates text from multiple languages to English, generates images, and creates creative text."
)

# Launch the Gradio app
interface.launch()