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| import streamlit as st | |
| import requests | |
| import os | |
| import time # Import time module for implementing delay | |
| # Fetch Hugging Face and Groq API keys from secrets | |
| Transalate_token = os.getenv('HUGGINGFACE_TOKEN') | |
| Image_Token = os.getenv('HUGGINGFACE_TOKEN') | |
| Content_Token = os.getenv('GROQ_API_KEY') | |
| Image_prompt_token = os.getenv('GROQ_API_KEY') | |
| # 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 = { | |
| "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" | |
| } | |
| # Default content generation model | |
| default_content_model = "llama-3.1-70b-versatile" | |
| # Function to query Hugging Face translation model with retry mechanism | |
| def translate_text(text): | |
| payload = {"inputs": text} | |
| max_retries = 3 # Maximum number of retry attempts | |
| delay = 2 # Delay between retries in seconds | |
| for attempt in range(max_retries): | |
| 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: | |
| st.warning(f"Translation failed (Attempt {attempt+1}/{max_retries}) - Retrying in {delay} seconds...") | |
| time.sleep(delay) # Wait for 2 seconds before retrying | |
| # If all retries fail, show an error | |
| st.error(f"Translation failed after {max_retries} attempts. Please reload the page and try again later.") | |
| return None | |
| # Function to query Groq content generation model | |
| 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 | |
| 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 | |
| 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: | |
| ... (omitted for brevity) | |
| """) | |
| # 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 | |
| # Custom CSS for background, borders, and other styling | |
| st.markdown( | |
| """ | |
| <style> | |
| body { | |
| background-image: url('https://wallpapercave.com/wp/wp4008910.jpg'); | |
| background-size: cover; | |
| } | |
| .reportview-container { | |
| background: rgba(255, 255, 255, 0.85); | |
| padding: 2rem; | |
| border-radius: 10px; | |
| box-shadow: 0px 0px 20px rgba(0, 0, 0, 0.1); | |
| } | |
| .result-container { | |
| border: 2px solid #4CAF50; | |
| padding: 20px; | |
| border-radius: 10px; | |
| margin-top: 20px; | |
| animation: fadeIn 2s ease; | |
| } | |
| @keyframes fadeIn { | |
| 0% { opacity: 0; } | |
| 100% { opacity: 1; } | |
| } | |
| .stButton button { | |
| background-color: #4CAF50; | |
| color: white; | |
| border-radius: 10px; | |
| padding: 10px; | |
| } | |
| .stButton button:hover { | |
| background-color: #45a049; | |
| transform: scale(1.05); | |
| transition: 0.2s ease-in-out; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True | |
| ) | |
| 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) | |
| # Reminder about model availability | |
| st.sidebar.warning("Note: Based on availability, some models might not work. Please try another model if an error occurs.By default the perfect model is selected try with it and then experiment with different models") | |
| # 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(tamil_input) | |
| if english_text: | |
| st.write(english_text) | |
| # Step 2: Content Generation | |
| st.write("### Educational Content Generated:") | |
| content = generate_content(english_text, max_tokens, temperature, content_models[content_model]) | |
| if content: | |
| st.write(content) | |
| # Step 3: Generate Image Prompt | |
| st.write("### Image Prompt:") | |
| image_prompt = generate_image_prompt(english_text) | |
| if image_prompt: | |
| st.write(image_prompt) | |
| # Step 4: Image Generation | |
| st.write("### Generated Image:") | |
| image = generate_image(image_prompt, image_generation_urls[image_model]) | |
| if image: | |
| st.image(image) | |
| else: | |
| st.error("Please enter or select Tamil text.") | |
| if __name__ == "__main__": | |
| main() | |