app.py

import streamlit as st
from streamlit_option_menu import option_menu
from transformers import pipeline
import torch
import time
import requests
import io
import os
from PIL import Image

# Load models
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-dra-en")

# for summarizer api
SUMMARIZER_API_URL = "https://api.groq.com/openai/v1/chat/completions"
summarizer_headers = {"Authorization": f"Bearer {os.getenv('GROQ_API_TOKEN')}",
                     "Content-Type": "application/json"}


# for image api
IMAGE_API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
img_headers = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}


# Functions for each task
def translate_tamil_to_english(text):
    time.sleep(2)
    result = translator(text)
    return result[0]['translation_text']

def summarize_english_text(paragraph):
    time.sleep(2)
    # Request payload
    payload = {
        "model": "mixtral-8x7b-32768",
        "messages": [
            {"role": "system", "content": "Create a summary of below paragraph in 30 words max"},
            {"role": "user", "content": paragraph}
        ],
        "max_tokens": 100  # number of words in the output.
    }

    # Send POST request to Groq API
    response = requests.post(SUMMARIZER_API_URL, json=payload, headers=summarizer_headers)

    # Check if the request was successful
    if response.status_code == 200:
        # Parse the JSON response
        result = response.json()
        # Extract and print the generated text
        generated_text = result['choices'][0]['message']['content']
        return generated_text
    else:
        return f"Error: {response.status_code}, {response.text}"

def english_text_to_image(prompt):
    payload = {
        "inputs": prompt,
    }
    response = requests.post(IMAGE_API_URL, headers=img_headers, json=payload)
    image_bytes = response.content
    image = Image.open(io.BytesIO(image_bytes))
    return image

# Custom CSS
# st.markdown("""
#     <style>
#         /* Background color */
#         body {
#             background-color: #f0f0f5;
#         }

#         /* Text color and font */
#         .stApp {
#             font-family: 'Arial', sans-serif;
#             color: #333;
#         }

#         /* Titles and subtitles styling */
#         h1 {
#             color: #2E8B57;
#             text-align: center;
#             text-shadow: 2px 2px 5px #aaaaaa;
#         }

#         h2, h3 {
#             color: #4682B4;
#             text-shadow: 1px 1px 3px #aaaaaa;
#         }

#         /* Background texture */
#         .stApp {
#             background: linear-gradient(to bottom right, #fff7e6, #e6f7ff);
#         }

#         /* Button styling */
#         button[kind="primary"] {
#             background-color: #4682B4;
#             color: white;
#             border-radius: 8px;
#             padding: 0.5rem 1rem;
#         }

#         button[kind="primary"]:hover {
#             background-color: #5b9bd5;
#         }

#         /* Text area and input field styling */
#         textarea, input {
#             border-radius: 10px;
#             padding: 1rem;
#             border: 2px solid #ccc;
#             background-color: #f9f9f9;
#         }

#         /* Styling the output boxes */
#         .stMarkdown {
#             background-color: #e6f9ff;
#             padding: 1rem;
#             border-radius: 10px;
#             box-shadow: 2px 2px 10px #ccc;
#         }
#     </style>
#     """, unsafe_allow_html=True)


# #sidebar styling 
# st.markdown("""
# <style>
#     [data-testid=stSidebar] {
#         background-color: #FFFFFF;
#         margin-right: 20px;
#         border-right: 2px solid #FFFFFF
#     }
# </style>
# """, unsafe_allow_html=True)

#options styling in sidebar and added image in sidebar
with st.sidebar:
    selected = option_menu(
        menu_title="",
        options=['Home','Tool'],
        icons=['house-door-fill','setting'],
        menu_icon='truck-front-fill',
        default_index=0,
        styles={
            "container": {'padding':'5!important','background-color':'#FAF9F6'},
            "icon": {'color':"#000000", "font-size":"23px"},
            "nav-link": {'font-size':'16px','text-align':'left','margin':'0px','--hover-color':'#EDEADE','font-weight':'bold'},
            "nav-link-selector":{'background-color':'#E6E6FA','font-weight':'bold'}
        }
    )


if selected == "Home":
    # Page title and header
    st.title(":blue[Multi-Purpose Tool] - Empowering Educators 🎓")

    # Subheader for the app description
    st.subheader("A versatile tool designed to assist teachers in translating, summarizing, and visualizing concepts.")

    # Main description with detailed information about the app
    st.markdown("""
    The **Multi-Purpose Tool** is a user-friendly platform developed for educators, 
    enabling them to enhance their teaching experience. Whether it's translating content 
    into different languages, summarizing lengthy materials, or visualizing concepts 
    through images, this tool provides a one-stop solution for modern teaching needs.
    
    ### Key Features:
    - **Translation**: Translate text seamlessly between languages (e.g., Tamil to English).
    - **Summarization**: Quickly generate summaries of long passages for easy understanding.
    - **Text to Image**: Visualize difficult concepts by generating images from text descriptions.
    
    ### Available Worldwide:
    The Multi-Purpose Tool is deployed on Hugging Face and accessible globally to teachers 
    and educators at the click of a button. Visit the [app here](https://huggingface.co/spaces/Jesivn/Multi_purpose_Software).
    
    Empower your classroom with advanced AI tools today!
    """)
elif selected=="Tool":    
    # Row 1: Tamil to English translation
    st.subheader("🌐 Translate Tamil to English")
    tamil_input = st.text_area("Enter Tamil text", "")
    if st.button("Translate"):
        english_output = translate_tamil_to_english(tamil_input)
        st.markdown(f"**Translated English Text**: {english_output}")
    
    # Row 2: English paragraph summarization
    st.subheader("📝 Summarize English Paragraph")
    english_paragraph = st.text_area("Enter English paragraph", "")
    if st.button("Summarize"):
        summary_output = summarize_english_text(english_paragraph)
        st.markdown(f"**Summary**: {summary_output}")
    
    # Row 3: English text to image generation
    st.subheader("🎨 Generate Image from English Text")
    image_text = st.text_input("Enter description for image generation", "")
    if st.button("Generate Image"):
        generated_image = english_text_to_image(image_text)
        st.image(generated_image, caption="Generated Image")