Update app.py

app.py

@@ -1,151 +1,109 @@
 import streamlit as st
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 import torch
-import os
 
-# Custom CSS styling for a light, elegant design
+# Load model and tokenizer
+model_path = "./saved_model"
+tokenizer_path = "./saved_tokenizer"
+
+tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+
+# Function to generate summary
+def generate_summary(text):
+    inputs = ["summarize: " + text]
+    inputs = tokenizer(inputs, max_length=1024, truncation=True, return_tensors="pt")
+    outputs = model.generate(inputs.input_ids.to(model.device), max_length=150, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Custom CSS styling for a clean, modern interface
 st.markdown("""
 <style>
     .main {
-        background-color: #FAFAFA;
-        background-image: linear-gradient(135deg, #ffffff 0%, #e0f7fa 100%);
+        background-color: #f0f2f6;
+        background-image: linear-gradient(135deg, #e6f7ff 0%, #b3e0ff 100%);
+        font-family: 'Arial', sans-serif;
     }
     .stTextArea textarea {
-        border: 2px solid #81D4FA;
-        border-radius: 15px;
-        padding: 10px;
+        border: 2px solid #0078d4;
+        border-radius: 12px;
+        padding: 15px;
         font-family: 'Segoe UI', sans-serif;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
         width: 100%;
         max-width: 800px;
-        margin: 0 auto;
+        margin: 20px auto;
+        font-size: 16px;
     }
     .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;
+        border-color: #0058a3;
+        box-shadow: 0 0 10px #4f8bf9;
     }
     .stButton>button {
-        background-color: #29B6F6;
+        background-color: #29b6f6;
         color: white;
-        border-radius: 20px;
+        border-radius: 25px;
         border: none;
-        padding: 10px 25px;
-        font-size: 16px;
+        padding: 12px 30px;
+        font-size: 18px;
         font-weight: bold;
-        box-shadow: 0 4px 12px rgba(41, 182, 246, 0.4);
+        box-shadow: 0 4px 12px rgba(41, 182, 246, 0.3);
         transition: all 0.3s ease;
     }
     .stButton>button:hover {
-        background-color: #0288D1;
-        box-shadow: 0 6px 14px rgba(2, 136, 209, 0.5);
+        background-color: #0288d1;
+        box-shadow: 0 6px 14px rgba(2, 136, 209, 0.4);
         transform: translateY(-2px);
     }
+    .stTitle {
+        color: #0078d4;
+        font-size: 2.5em;
+        text-align: center;
+        margin-bottom: 20px;
+        font-family: 'Segoe UI', sans-serif;
+    }
     .summary-container {
         background-color: #ffffff;
-        border-radius: 15px;
+        border-radius: 12px;
         padding: 20px;
-        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.05);
-        border-left: 5px solid #29B6F6;
+        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
         margin-top: 20px;
-        width: 100%;
         max-width: 800px;
-        margin-left: auto;
-        margin-right: auto;
+        margin: 20px auto;
     }
     .summary-title {
-        color: #0288D1;
+        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;
+        color: #0078d4;
         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")
-        
-        # Add the custom token and ensure the tokenizer is saved properly
-        added_tokens = tokenizer.add_tokens(['<extra_id_99>'])  # Add custom token if required
-        
-        # Ensure the tokenization process handles the vocabulary correctly
-        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)
-    model.resize_token_embeddings(len(tokenizer))  # Resize the embeddings to match tokenizer size
-    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)
+# Application UI
+st.title("📝 Text Summarizer App")
 
 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.")
+if st.button("Generate Summary"):
+    if text:
+        with st.spinner("Generating summary..."):
+            summary = generate_summary(text)
+            st.markdown('<div class="summary-container"><div class="summary-title">📋 Summary</div>' + 
+                        summary + '</div>', unsafe_allow_html=True)
+    else:
+        st.warning("⚠️ Please enter text to summarize.")
 
+# Footer
 st.markdown("""
 <div class="footer">
-    Smart Text Summarizer - Crafted with hadheedo
+    Created with hadheedo
 </div>
 """, unsafe_allow_html=True)