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("""
    ### Welcome to FusionMind Multimodel ---> Your one stop solution for content creation.
    
    
    ***How to use this app:***

1. **Input Tamil Text**: 
   - You can either select one of the suggested Tamil phrases or input your own text. The app primarily focuses on Tamil inputs, but it supports a wide range of other languages as well (see the list below).
   
2. **Generate Translations**: 
   - Once you've input your text, the app will automatically translate it to English. The translation model is a **many-to-one model**, meaning it can take input from various languages and translate it into English.

3. **Generate Educational Content**: 
   - After translating the text into English, the app will generate **educational content** based on the translated input. You can adjust the creativity of the content generation using the temperature slider, and control the length of the output with the token limit setting.

4. **Generate Images**: 
   - In addition to generating content, the app can also generate an **image** related to the translated content. You don’t need to worry about creating complex image prompts—FusionMind includes an automatic **image prompt generator** that will convert your input into a well-defined image prompt, ensuring better image generation results.

---

### Features:

- **Multilingual Translation**: 
   - FusionMind supports a **many-to-one translation model**, so you can input text in a wide variety of languages, not just Tamil. Below are the supported languages:
   
     - **Arabic (ar_AR)**, **Czech (cs_CZ)**, **German (de_DE)**, **English (en_XX)**, **Spanish (es_XX)**, **Estonian (et_EE)**, **Finnish (fi_FI)**, **French (fr_XX)**, **Gujarati (gu_IN)**, **Hindi (hi_IN)**, **Italian (it_IT)**, **Japanese (ja_XX)**, **Kazakh (kk_KZ)**, **Korean (ko_KR)**, **Lithuanian (lt_LT)**, **Latvian (lv_LV)**, **Burmese (my_MM)**, **Nepali (ne_NP)**, **Dutch (nl_XX)**, **Romanian (ro_RO)**, **Russian (ru_RU)**, **Sinhala (si_LK)**, **Turkish (tr_TR)**, **Vietnamese (vi_VN)**, **Chinese (zh_CN)**, **Afrikaans (af_ZA)**, **Azerbaijani (az_AZ)**, **Bengali (bn_IN)**, **Persian (fa_IR)**, **Hebrew (he_IL)**, **Croatian (hr_HR)**, **Indonesian (id_ID)**, **Georgian (ka_GE)**, **Khmer (km_KH)**, **Macedonian (mk_MK)**, **Malayalam (ml_IN)**, **Mongolian (mn_MN)**, **Marathi (mr_IN)**, **Polish (pl_PL)**, **Pashto (ps_AF)**, **Portuguese (pt_XX)**, **Swedish (sv_SE)**, **Swahili (sw_KE)**, **Tamil (ta_IN)**, **Telugu (te_IN)**, **Thai (th_TH)**, **Tagalog (tl_XX)**, **Ukrainian (uk_UA)**, **Urdu (ur_PK)**, **Xhosa (xh_ZA)**, **Galician (gl_ES)**, **Slovene (sl_SI)**.

- **Temperature Adjustment**: 
   - You can adjust the **temperature** of the content generation. A **higher temperature** makes the content more creative and varied, while a **lower temperature** generates more focused and deterministic responses.

- **Token Limit**: 
   - Set the **maximum number of tokens** for content generation. This allows you to control the length of the generated educational content.

- **Automatic Retries**: 
   - If a translation request fails due to any reason, the app is designed to **automatically retry**, ensuring a smooth experience.

- **Auto-Generated Image Prompts**: 
   - One of the unique features of FusionMind is the **auto-generated image prompts**. Even if you're not experienced in creating detailed prompts for image generation, the app will take care of this for you. It automatically converts the translated text or content into a well-defined prompt that produces more accurate and high-quality images.

---

Enjoy the multimodal experience with **FusionMind** and explore its powerful translation, content generation, and image generation features!

    """)

# 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()