app.py

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()