Delete TransArt_Gradio_Code

TransArt_Gradio_Code

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-# Install the required libraries
-!pip install transformers gradio Pillow requests
-import os
-import requests
-from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
-from PIL import Image, ImageDraw
-import io
-import gradio as gr
-import torch
-
-# Detect if GPU is available
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# Load the MarianMT model and tokenizer for translation (Tamil to English)
-model_name = "Helsinki-NLP/opus-mt-mul-en"
-translation_model = MarianMTModel.from_pretrained(model_name).to(device)
-translation_tokenizer = MarianTokenizer.from_pretrained(model_name)
-
-# Load GPT-Neo for creative text generation
-text_generation_model_name = "EleutherAI/gpt-neo-1.3B"
-text_generation_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name).to(device)
-text_generation_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
-
-# Add padding token to GPT-Neo tokenizer if not present
-if text_generation_tokenizer.pad_token is None:
-    text_generation_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-
-# Set your Hugging Face API key
-os.environ['HF_API_KEY'] = 'Your_HF_TOKEN'  # Replace with your actual API key
-api_key = os.getenv('HF_API_KEY')
-if api_key is None:
-    raise ValueError("Hugging Face API key is not set. Please set it in your environment.")
-
-headers = {"Authorization": f"Bearer {api_key}"}
-
-# Define the API URL for image generation (replace with actual model URL)
-API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"  # Replace with a valid image generation model
-
-# Query Hugging Face API to generate image with error handling
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    if response.status_code != 200:
-        print(f"Error: Received status code {response.status_code}")
-        print(f"Response: {response.text}")
-        return None
-    return response.content
-
-# Translate Tamil text to English
-def translate_text(tamil_text):
-    inputs = translation_tokenizer(tamil_text, return_tensors="pt", padding=True, truncation=True).to(device)
-    translated_tokens = translation_model.generate(**inputs)
-    translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
-    return translation
-
-# Generate an image based on the translated text with error handling
-def generate_image(prompt):
-    image_bytes = query({"inputs": prompt})
-
-    if image_bytes is None:
-        # Return a blank image with error message
-        error_img = Image.new('RGB', (300, 300), color=(255, 0, 0))
-        d = ImageDraw.Draw(error_img)
-        d.text((10, 150), "Image Generation Failed", fill=(255, 255, 255))
-        return error_img
-
-    try:
-        image = Image.open(io.BytesIO(image_bytes))
-        return image
-    except Exception as e:
-        print(f"Error: {e}")
-        # Return an error image in case of failure
-        error_img = Image.new('RGB', (300, 300), color=(255, 0, 0))
-        d = ImageDraw.Draw(error_img)
-        d.text((10, 150), "Invalid Image Data", fill=(255, 255, 255))
-        return error_img
-
-# Generate creative text based on the translated English text
-def generate_creative_text(translated_text):
-    inputs = text_generation_tokenizer(translated_text, return_tensors="pt", padding=True, truncation=True).to(device)
-    generated_tokens = text_generation_model.generate(**inputs, max_length=100)
-    creative_text = text_generation_tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
-    return creative_text
-
-# Function to handle the full workflow
-def translate_generate_image_and_text(tamil_text):
-    # Step 1: Translate Tamil to English
-    translated_text = translate_text(tamil_text)
-
-    # Step 2: Generate an image from the translated text
-    image = generate_image(translated_text)
-
-    # Step 3: Generate creative text from the translated text
-    creative_text = generate_creative_text(translated_text)
-
-    return translated_text, creative_text, image
-
-# Create a visually appealing Gradio interface
-css = """
-#transart-title {
-    font-size: 2.5em;
-    font-weight: bold;
-    color: #4CAF50;
-    text-align: center;
-    margin-bottom: 10px;
-}
-#transart-subtitle {
-    font-size: 1.25em;
-    text-align: center;
-    color: #555555;
-    margin-bottom: 20px;
-}
-body {
-    background-color: #f0f0f5;
-}
-.gradio-container {
-    font-family: 'Arial', sans-serif;
-}
-"""
-
-# Custom HTML for title and subtitle (can be displayed in Markdown)
-title_markdown = """
-# <div id="transart-title">TransArt</div>
-### <div id="transart-subtitle">Tamil to English Translation, Creative Text & Image Generation</div>
-"""
-
-# Gradio interface with customized layout and aesthetics
-with gr.Blocks(css=css) as interface:
-    gr.Markdown(title_markdown)  # Title and subtitle in Markdown
-    with gr.Row():
-        with gr.Column():
-            tamil_input = gr.Textbox(label="Enter Tamil Text", placeholder="Type Tamil text here...", lines=3)  # Input for Tamil text
-        with gr.Column():
-            translated_output = gr.Textbox(label="Translated Text", interactive=False)        # Output for translated text
-            creative_text_output = gr.Textbox(label="Creative Generated Text", interactive=False)  # Output for creative text
-            generated_image_output = gr.Image(label="Generated Image")  # Output for generated image
-
-    gr.Button("Generate").click(fn=translate_generate_image_and_text, inputs=tamil_input, outputs=[translated_output, creative_text_output, generated_image_output])
-
-# Launch the Gradio app
-interface.launch(debug=True, server_name="0.0.0.0")