app.py

import streamlit as st
import requests
import os

# 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 URL
image_generation_url = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"

# Function to query Hugging Face translation model with try-except retry logic
def translate_text(text):
    payload = {"inputs": text}
    
    # Try block to handle the first attempt
    try:
        response = requests.post(translation_url, headers=Translate, json=payload)
        response.raise_for_status()  # Raise an error for bad status codes (non-200)
        result = response.json()
        translated_text = result[0]['generated_text']
        return translated_text

    except requests.exceptions.RequestException as e:
        st.warning(f"First attempt failed due to: {e}. Retrying...")

        # Retry the request once if it fails
        try:
            response = requests.post(translation_url, headers=Translate, json=payload)
            response.raise_for_status()  # Raise an error for bad status codes (non-200)
            result = response.json()
            translated_text = result[0]['generated_text']
            return translated_text

        except requests.exceptions.RequestException as e:
            st.error(f"Second attempt failed: {e}")
            return None

# Function to query Groq content generation model
def generate_content(english_text, max_tokens, temperature):
    url = "https://api.groq.com/openai/v1/chat/completions"
    payload = {
        "model": "llama-3.1-70b-versatile",
        "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):
    data = {"inputs": image_prompt}
    response = requests.post(image_generation_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 content
def user_guide():
    st.title("User Guide")
    st.write("""
    ### How to use this app:

    1. **Input Tamil Text**: You can either select one of the suggested Tamil phrases or input your own.
    2. **Generate Translations**: The app will automatically translate Tamil input to English.
    3. **Generate Educational Content**: Based on the translated text, the app will generate educational content.
    4. **Generate Images**: The app will also generate an image based on the content.

    ### Features:
    - **Temperature Adjustment**: You can adjust the temperature for content creativity.
    - **Token Limit**: Set the maximum number of tokens for content generation.
    - **Retries**: If the translation fails, the app will retry automatically.
    
    Enjoy the multimodal experience with FusionMind!
    """)

# Main Streamlit app
def main():
    # Sidebar for navigation
    st.sidebar.title("Navigation")
    page = st.sidebar.radio("Go to", ["Home", "User Guide"])

    # If user selects "User Guide" page
    if page == "User Guide":
        user_guide()
    else:
        # 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 and token adjustment
        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)

        # Suggested inputs
        st.write("## Suggested Inputs")
        suggestions = ["தரவு அறிவியல்", "புதிய திறன்களைக் கற்றுக்கொள்வது எப்படி", "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.success(english_text)

                    # Step 2: Generate Educational Content
                    st.write("### Generated Educational Content:")
                    with st.spinner('Generating content...'):
                        content_output = generate_content(english_text, max_tokens, temperature)
                        if content_output:
                            st.success(content_output)

                    # Step 3: Generate Image from the prompt
                    st.write("### Generated Image:")
                    with st.spinner('Generating image...'):
                        image_prompt = generate_image_prompt(english_text)
                        image_data = generate_image(image_prompt)
                        if image_data:
                            st.image(image_data, caption="Generated Image")

if __name__ == "__main__":
    main()