# Import required libraries import gradio as gr import requests from getpass import getpass import openai from PIL import Image import io # Input your Hugging Face and Groq tokens securely Transalate_token = getpass("Enter Hugging Face Translation Token: ") Image_Token = getpass("Enter Hugging Face Image Generation Token: ") Content_Token = getpass("Enter Groq Content Generation Token: ") Image_prompt_token = getpass("Enter Groq Prompt Generation Token: ") # API Keys for GPT and Gemini (replace with your actual keys) openai.api_key = getpass("Enter OpenAI API Key: ") # gemini_token = getpass("Enter Gemini API Key: ") # Placeholder, you will need API access # API Headers Translate = {"Authorization": f"Bearer {Transalate_token}"} Image_generation = {"Authorization": f"Bearer {Image_Token}"} Content_generation = { "Authorization": f"Bearer {Content_Token}", "Content-Type": "application/json" } Image_Prompt = { "Authorization": f"Bearer {Image_prompt_token}", "Content-Type": "application/json" } # Translation Model API URL (Tamil to English) translation_url = "https://api-inference.huggingface.co/models/facebook/mbart-large-50-many-to-one-mmt" # Text-to-Image Model API URLs image_generation_urls = { "black-forest-labs/FLUX.1-schnell": "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell", "CompVis/stable-diffusion-v1-4": "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4", "black-forest-labs/FLUX.1-dev": "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" } # Default image generation model default_image_model = "black-forest-labs/FLUX.1-schnell" # Content generation models content_models = { "GPT-4 (OpenAI)": "gpt-4", "Gemini-1 (DeepMind)": "gemini-1", "llama-3.1-70b-versatile": "llama-3.1-70b-versatile", "mixtral-8x7b-32768": "mixtral-8x7b-32768" } # Default content generation model default_content_model = "GPT-4 (OpenAI)" # Function to query Hugging Face translation model def translate_text(text): payload = {"inputs": text} response = requests.post(translation_url, headers=Translate, json=payload) if response.status_code == 200: result = response.json() translated_text = result[0]['generated_text'] return translated_text else: return f"Translation Error {response.status_code}: {response.text}" # Function to generate content using GPT or Gemini def generate_content(english_text, max_tokens, temperature, model): if model == "gpt-4": # Using OpenAI's GPT model response = openai.Completion.create( engine=model, # GPT model (like gpt-4) prompt=f"Write educational content about {english_text} within {max_tokens} tokens.", max_tokens=max_tokens, temperature=temperature ) return response.choices[0].text.strip() # elif model == "gemini-1": # # Placeholder: Add code to call Gemini API here # # Using the Gemini API (this requires the correct endpoint and token from Google DeepMind) # # For example, you would create a POST request similar to OpenAI's API. # url = "https://api.deepmind.com/gemini/v1/generate" # headers = { # "Authorization": f"Bearer {gemini_token}", # "Content-Type": "application/json" # } # payload = { # "model": "gemini-1", # "input": f"Write educational content about {english_text} within {max_tokens} tokens.", # "temperature": temperature, # "max_tokens": max_tokens # } # response = requests.post(url, json=payload, headers=headers) # if response.status_code == 200: # return response.json()['choices'][0]['text'] # else: # return f"Gemini Content Generation Error {response.status_code}: {response.text}" else: # Default to the Groq API or other models if selected url = "https://api.groq.com/openai/v1/chat/completions" payload = { "model": model, "messages": [ {"role": "system", "content": "You are a creative and insightful writer."}, {"role": "user", "content": f"Write educational content about {english_text} within {max_tokens} tokens."} ], "max_tokens": max_tokens, "temperature": temperature } response = requests.post(url, json=payload, headers=Content_generation) if response.status_code == 200: result = response.json() return result['choices'][0]['message']['content'] else: return f"Content Generation Error: {response.status_code}" # Function to generate image prompt def generate_image_prompt(english_text): payload = { "model": "mixtral-8x7b-32768", "messages": [ {"role": "system", "content": "You are a professional Text to image prompt generator."}, {"role": "user", "content": f"Create a text to image generation prompt about {english_text} within 30 tokens."} ], "max_tokens": 30 } response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=Image_Prompt) if response.status_code == 200: result = response.json() return result['choices'][0]['message']['content'] else: return f"Prompt Generation Error: {response.status_code}" # Function to generate an image from the prompt def generate_image(image_prompt, model_url): data = {"inputs": image_prompt} response = requests.post(model_url, headers=Image_generation, json=data) if response.status_code == 200: # Convert the image bytes to a PIL Image image = Image.open(io.BytesIO(response.content)) # Save image to a temporary file-like object for Gradio image.save("/tmp/generated_image.png") # Save the image to a file return "/tmp/generated_image.png" # Return the path to the image else: return f"Image Generation Error {response.status_code}: {response.text}" # Gradio App def fusionmind_app(tamil_input, temperature, max_tokens, content_model, image_model): # Step 1: Translation (Tamil to English) english_text = translate_text(tamil_input) # Step 2: Generate Educational Content content_output = generate_content(english_text, max_tokens, temperature, content_models[content_model]) # Step 3: Generate Image from the prompt image_prompt = generate_image_prompt(english_text) image_data = generate_image(image_prompt, image_generation_urls[image_model]) return english_text, content_output, image_data # Gradio Interface interface = gr.Interface( fn=fusionmind_app, inputs=[ gr.Textbox(label="Enter Tamil Text"), gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"), gr.Slider(minimum=100, maximum=400, value=200, label="Max Tokens for Content Generation"), gr.Dropdown(list(content_models.keys()), label="Select Content Generation Model", value=default_content_model), gr.Dropdown(list(image_generation_urls.keys()), label="Select Image Generation Model", value=default_image_model) ], outputs=[ gr.Textbox(label="Translated English Text"), gr.Textbox(label="Generated Content"), gr.Image(label="Generated Image") # Display the generated image ], title="TransArt: A Multimodal Application for Vernacular Language Translation and Image Synthesis", description="Translate Tamil to English, generate educational content, and generate related images!" ) # Launch Gradio App interface.launch(debug=True)