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 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
def translate_text(text):
    payload = {"inputs": text}
    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.error(f"Translation Error {response.status_code}: {response.text}")
        st.write(f'Please try after sometime 😥😥😥')
        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

# Main Streamlit app
def main():
    # 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.")

    # 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, content_models[content_model])
                    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, image_generation_urls[image_model])
                    if image_data:
                        st.image(image_data, caption="Generated Image")

if __name__ == "__main__":
    main()