Create app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn.functional as F
+
+# Import GPT2 Model and Tokenizer
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Import T5 Model and Tokenizer
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+st.title("Text Summarizer")
+
+models = {
+    "T5 Small": "ZinebSN/T5_summarizer",
+    "GPT2": "ZinebSN/GPT2_summarizer"
+}
+
+selected_model = st.radio("Select Model", list(models.keys()))
+
+model_name = models[selected_model]
+
+if selected_model=='GPT2':
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+    model = GPT2LMHeadModel.from_pretrained(model_name)
+else:
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    
+# Inference function for GPT2
+def gpt2_summarize(input_text, tokenizer, model, length):
+    text=tokenizer.encode_plus(f'<bos> {input_text} <sep>', truncation=True, max_length=1024).input_ids
+    text_length=len(text)
+    text = torch.tensor(text, dtype=torch.long, device=device)
+    text = text.unsqueeze(0)
+    generated = text
+    model = model.to(device)
+    with torch.no_grad():
+        for _ in range(length):
+            inputs = {'input_ids': generated}
+            outputs = model(**inputs)
+            next_token_logits = outputs[0][0, -1, :]
+            next_token = torch.multinomial(F.softmax(next_token_logits, dim=-1), num_samples=1)
+            generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)
+            generated=generated[:, -1024:]            
+    generated = generated[0, text_length:]
+    text = tokenizer.convert_ids_to_tokens(generated,skip_special_tokens=True)
+    text = tokenizer.convert_tokens_to_string(text)
+    return text
+
+# Inference function for T5
+def t5_summarize(input_text, tokenizer, model):
+    inputs=tokenizer('summarize: '+input_text, truncation=True, padding='max_length', max_length=600, return_tensors='pt')
+    output_sequence=model.generate(input_ids=inputs["input_ids"],attention_mask=inputs["attention_mask"], max_new_tokens=100)
+    summary = tokenizer.batch_decode(output_sequence, skip_special_tokens=True)
+    return summary[0]
+    
+
+input_text=st.text_area("Input the text to summarize","", height=300)
+if st.button("Summarize"):
+    st.text("It may take a minute or two.")
+    nwords=len(input_text.split(" "))
+    if selected_model=='GPT2':
+        summary=gpt2_summarize(input_text, tokenizer, model, 30)
+    else:
+        summary=t5_summarize(input_text, tokenizer, model)
+    
+    st.header("Summary")
+    st.markdown(summary)