Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import requests | |
| import os | |
| from transformers import MBartForConditionalGeneration, MBart50TokenizerFast | |
| # API keys for other features (optional) | |
| Image_Token = os.getenv('Hugging_face_token') | |
| Content_Token = os.getenv('Groq_api') | |
| Image_prompt_token = os.getenv('Groq_api') | |
| # API Headers for external services (optional) | |
| 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" | |
| } | |
| # 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 content generation model | |
| content_models = { | |
| "llama-3.1-70b-versatile": "llama-3.1-70b-versatile", | |
| "llama3-8b-8192": "llama3-8b-8192", | |
| "gemma2-9b-it": "gemma2-9b-it", | |
| "mixtral-8x7b-32768": "mixtral-8x7b-32768" | |
| } | |
| # Load the translation model and tokenizer locally | |
| def load_translation_model(): | |
| model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt") | |
| tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt") | |
| return model, tokenizer | |
| # Function to perform translation locally | |
| def translate_text_local(text): | |
| model, tokenizer = load_translation_model() | |
| inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True) | |
| translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"]) | |
| translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0] | |
| return translated_text | |
| # Function to query Groq content generation model (optional) | |
| def generate_content(english_text, max_tokens, temperature, model): | |
| 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: | |
| st.error(f"Content Generation Error: {response.status_code}") | |
| return None | |
| # Function to generate image prompt (optional) | |
| 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: | |
| st.error(f"Prompt Generation Error: {response.status_code}") | |
| return None | |
| # Function to generate an image from the prompt (optional) | |
| 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: | |
| return response.content | |
| else: | |
| st.error(f"Image Generation Error {response.status_code}: {response.text}") | |
| return None | |
| # User Guide Section | |
| def show_user_guide(): | |
| st.title("FusionMind User Guide") | |
| st.write(""" | |
| ### Welcome to the FusionMind User Guide! | |
| ### How to use this app: | |
| 1. **Input Tamil Text**: Enter Tamil text to be translated and processed. | |
| 2. **Generate Translations**: The app will automatically translate Tamil to English. | |
| 3. **Generate Educational Content**: Use the translated English text to generate content. | |
| 4. **Generate Images**: Optionally, generate an image related to the translated text. | |
| """) | |
| # Main Streamlit app | |
| def main(): | |
| # Sidebar Menu | |
| st.sidebar.title("FusionMind Options") | |
| page = st.sidebar.radio("Select a page:", ["Main App", "User Guide"]) | |
| if page == "User Guide": | |
| show_user_guide() | |
| return | |
| st.title("🅰️ℹ️ FusionMind ➡️ Multimodal") | |
| # Sidebar for temperature, token adjustment, and model selection | |
| st.sidebar.header("Settings") | |
| temperature = st.sidebar.slider("Select Temperature", 0.1, 1.0, 0.7) | |
| max_tokens = st.sidebar.slider("Max Tokens for Content Generation", 100, 400, 200) | |
| # Content generation model selection | |
| content_model = st.sidebar.selectbox("Select Content Generation Model", list(content_models.keys()), index=0) | |
| # Image generation model selection | |
| image_model = st.sidebar.selectbox("Select Image Generation Model", list(image_generation_urls.keys()), index=0) | |
| # Suggested inputs | |
| st.write("## Suggested Inputs") | |
| suggestions = ["தரவு அறிவியல்", "உளவியல்", "ராக்கெட் எப்படி வேலை செய்கிறது"] | |
| selected_suggestion = st.selectbox("Select a suggestion or enter your own:", [""] + suggestions) | |
| # Input box for user | |
| tamil_input = st.text_input("Enter Tamil text (or select a suggestion):", selected_suggestion) | |
| if st.button("Generate"): | |
| # Step 1: Translation (Tamil to English) | |
| if tamil_input: | |
| st.write("### Translated English Text:") | |
| english_text = translate_text_local(tamil_input) | |
| if english_text: | |
| st.success(english_text) | |
| # Step 2: Generate Educational Content | |
| st.write("### Generated Content:") | |
| with st.spinner('Generating content...'): | |
| content_output = generate_content(english_text, max_tokens, temperature, content_models[content_model]) | |
| if content_output: | |
| st.success(content_output) | |
| # Step 3: Generate Image from the prompt (optional) | |
| st.write("### Generated Image:") | |
| with st.spinner('Generating image...'): | |
| image_prompt = generate_image_prompt(english_text) | |
| image_data = generate_image(image_prompt, image_generation_urls[image_model]) | |
| if image_data: | |
| st.image(image_data, caption="Generated Image") | |
| if __name__ == "__main__": | |
| main() | |