Update app.py

app.py

@@ -1,26 +1,123 @@
 import streamlit as st
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import (
+    AutoTokenizer, 
+    AutoModelForSeq2SeqGeneration, 
+    T5ForConditionalGeneration, 
+    T5Tokenizer
+)
 
-# Carregar o modelo T5-small e o tokenizer
-tokenizer = T5Tokenizer.from_pretrained("t5-small")
-model = T5ForConditionalGeneration.from_pretrained("t5-small")
+# Initialize session state for models if not already done
+if 'models_loaded' not in st.session_state:
+    # Load the main T5 model and tokenizer (using t5-base for better quality)
+    st.session_state.t5_tokenizer = T5Tokenizer.from_pretrained("t5-base")
+    st.session_state.t5_model = T5ForConditionalGeneration.from_pretrained("t5-base")
+    
+    # Load the paraphrasing model and tokenizer
+    st.session_state.paraphrase_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+    st.session_state.paraphrase_model = AutoModelForSeq2SeqGeneration.from_pretrained("facebook/bart-large-cnn")
+    
+    st.session_state.models_loaded = True
 
-# Título da página
-st.title("Humanizador de Texto")
+def paraphrase_text(text):
+    """
+    Apply paraphrasing to the input text using BART model
+    """
+    inputs = st.session_state.paraphrase_tokenizer.encode(
+        text,
+        return_tensors="pt",
+        max_length=512,
+        truncation=True
+    )
+    
+    outputs = st.session_state.paraphrase_model.generate(
+        inputs,
+        max_length=512,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9
+    )
+    
+    return st.session_state.paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Caixa de texto para o usuário digitar
-input_text = st.text_area("Cole seu texto de robô aqui:")
+def humanize_text(text):
+    """
+    Humanize the input text using T5 model
+    """
+    input_ids = st.session_state.t5_tokenizer(
+        f"humanize: {text}",
+        return_tensors="pt",
+        max_length=512,
+        truncation=True
+    ).input_ids
+    
+    outputs = st.session_state.t5_model.generate(
+        input_ids,
+        max_length=len(text) + 100,  # Dynamic length based on input
+        do_sample=True,
+        temperature=0.7,  # Increased creativity
+        top_p=0.9,       # Nucleus sampling
+        num_beams=4,     # Beam search for better quality
+        no_repeat_ngram_size=2  # Avoid repetition
+    )
+    
+    return st.session_state.t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Botão para humanizar
-if st.button("Humanizar"):
-    if input_text:
-        # Pedir ao robô para humanizar o texto
-        input_ids = tokenizer(f"humanize: {input_text}", return_tensors="pt").input_ids
-        outputs = model.generate(input_ids, max_length=512)
-        humanized_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# UI Components
+st.set_page_config(page_title="Advanced Text Humanizer", page_icon="🤖")
 
-        # Mostrar o texto humanizado
-        st.success("Texto humanizado:")
-        st.write(humanized_text)
+st.title("🤖 → 🧑 Advanced Text Humanizer")
+st.markdown("""
+This app transforms robotic text into more natural, human-like language using 
+advanced AI models. It combines T5 and BART models for better results.
+""")
+
+# Input area with expanded capabilities
+input_text = st.text_area(
+    "Cole seu texto de robô aqui:",
+    height=150,
+    help="Paste your text here to transform it into a more natural, human-like version."
+)
+
+# Advanced settings in sidebar
+with st.sidebar:
+    st.header("Advanced Settings")
+    use_paraphrase = st.checkbox("Enable Paraphrasing", value=True)
+    show_original = st.checkbox("Show Original Text", value=False)
+
+# Process button with error handling
+if st.button("Humanizar", type="primary"):
+    if not input_text:
+        st.warning("⚠️ Por favor, cole um texto de robô primeiro!")
     else:
-        st.warning("Por favor, cole um texto de robô primeiro!")
+        with st.spinner("Processando o texto..."):
+            try:
+                # First humanization pass
+                humanized_text = humanize_text(input_text)
+                
+                # Optional paraphrasing pass
+                if use_paraphrase:
+                    final_text = paraphrase_text(humanized_text)
+                else:
+                    final_text = humanized_text
+                
+                # Display results
+                st.success("✨ Texto humanizado:")
+                if show_original:
+                    st.text("Texto original:")
+                    st.info(input_text)
+                st.markdown("**Resultado:**")
+                st.write(final_text)
+                
+            except Exception as e:
+                st.error(f"❌ Ocorreu um erro durante o processamento: {str(e)}")
+
+# Footer
+st.markdown("---")
+st.markdown(
+    """
+    <div style='text-align: center'>
+        <small>Desenvolvido com ❤️ usando Streamlit e Transformers</small>
+    </div>
+    """,
+    unsafe_allow_html=True
+)