app.py

import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
import os

# Custom CSS styling for a light, elegant design
st.markdown("""
<style>
    .main {
        background-color: #FAFAFA;
        background-image: linear-gradient(135deg, #ffffff 0%, #e0f7fa 100%);
    }
    .stTextArea textarea {
        border: 2px solid #81D4FA;
        border-radius: 15px;
        padding: 10px;
        font-family: 'Segoe UI', sans-serif;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
        width: 100%;
        max-width: 800px;
        margin: 0 auto;
    }
    .stTextArea textarea:focus {
        border-color: #29B6F6;
        box-shadow: 0 0 10px #4FC3F7;
    }
    .stTitle {
        color: #0288D1;
        font-family: 'Segoe UI', sans-serif;
        font-size: 3em !important;
        text-align: center;
        margin-bottom: 30px !important;
    }
    .stButton>button {
        background-color: #29B6F6;
        color: white;
        border-radius: 20px;
        border: none;
        padding: 10px 25px;
        font-size: 16px;
        font-weight: bold;
        box-shadow: 0 4px 12px rgba(41, 182, 246, 0.4);
        transition: all 0.3s ease;
    }
    .stButton>button:hover {
        background-color: #0288D1;
        box-shadow: 0 6px 14px rgba(2, 136, 209, 0.5);
        transform: translateY(-2px);
    }
    .summary-container {
        background-color: #ffffff;
        border-radius: 15px;
        padding: 20px;
        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.05);
        border-left: 5px solid #29B6F6;
        margin-top: 20px;
        width: 100%;
        max-width: 800px;
        margin-left: auto;
        margin-right: auto;
    }
    .summary-title {
        color: #0288D1;
        font-weight: bold;
        font-size: 1.5em;
        margin-bottom: 10px;
        font-family: 'Segoe UI', sans-serif;
    }
    .footer {
        text-align: center;
        margin-top: 50px;
        padding: 20px;
        color: #0288D1;
        font-style: italic;
    }
</style>
""", unsafe_allow_html=True)

# Load model and tokenizer
model_path = "./saved_model"
tokenizer_path = "./saved_tokenizer"  # Define this path for saved tokenizer

try:
    # Check if the tokenizer is saved, if not, add custom tokens
    if not os.path.exists(tokenizer_path):
        tokenizer = T5Tokenizer.from_pretrained("t5-small")
        tokenizer.add_tokens(['<extra_id_99>'])  # Add custom token if required
        tokenizer.save_pretrained(tokenizer_path)
    else:
        tokenizer = T5Tokenizer.from_pretrained(tokenizer_path, local_files_only=True)

    model = T5ForConditionalGeneration.from_pretrained(model_path, local_files_only=True, ignore_mismatched_sizes=True)
    device = torch.device("cpu")
    model.to(device)
    model_loaded = True
except Exception as e:
    st.error(f"Error loading model: {e}")
    model_loaded = False

def generate_summary(text):
    try:
        inputs = ["summarize: " + text]
        inputs = tokenizer(inputs, max_length=1024, truncation=True, return_tensors="pt").to(device)
        outputs = model.generate(
            inputs.input_ids,
            max_length=150,
            length_penalty=2.0,
            num_beams=4,
            early_stopping=True
        )
        return tokenizer.decode(outputs[0], skip_special_tokens=True)
    except Exception as e:
        st.error(f"Error generating summary: {e}")
        return None

st.title("🧠 Smart Text Summarizer")

st.markdown("""
<div style="text-align: center; margin-bottom: 30px;">
    <img src="https://api.placeholder.com/300x150?text=Smart+Summary" width="300" class="header-image">
</div>
""", unsafe_allow_html=True)

text = st.text_area("Enter the text you want to summarize...", height=200)

col1, col2, col3 = st.columns([1, 2, 1])
with col2:
    if st.button("🔍 Generate Summary"):
        if text and model_loaded:
            with st.spinner("Generating summary..."):
                summary = generate_summary(text)
                if summary:
                    st.markdown('<div class="summary-container"><div class="summary-title">📋 Summary</div>' + 
                                summary + '</div>', unsafe_allow_html=True)
                else:
                    st.error("❌ Failed to generate summary. Please check your input.")
        elif not model_loaded:
            st.error("❌ Failed to load model. Please check the application logs.")
        else:
            st.warning("⚠️ Please enter text to summarize.")

st.markdown("""
<div class="footer">
    Smart Text Summarizer - Crafted with hadheedo
</div>
""", unsafe_allow_html=True)