Update app.py

app.py

@@ -1,48 +1,48 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
 
-# Load grammar model
+# Load grammar correction model
 grammar_model_name = "prithivida/grammar_error_correcter_v1"
 grammar_tokenizer = AutoTokenizer.from_pretrained(grammar_model_name)
 grammar_model = AutoModelForSeq2SeqLM.from_pretrained(grammar_model_name)
 
-# Load summarizer
+# Load summarization pipeline
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Load title generation model
+# Load title generation model (T5)
 title_model_name = "t5-base"
 title_tokenizer = AutoTokenizer.from_pretrained(title_model_name)
 title_model = AutoModelForSeq2SeqLM.from_pretrained(title_model_name)
 
-def polish_abstract(raw_text):
-    # 1. Grammar Correction
-    grammar_inputs = grammar_tokenizer.encode("gec: " + raw_text, return_tensors="pt", truncation=True, max_length=512)
-    grammar_outputs = grammar_model.generate(grammar_inputs, max_length=512, num_beams=5)
-    corrected_text = grammar_tokenizer.decode(grammar_outputs[0], skip_special_tokens=True)
+def polish_abstract(text):
+    # --- 1. Grammar Correction ---
+    grammar_input = grammar_tokenizer("gec: " + text, return_tensors="pt", max_length=512, truncation=True)
+    grammar_output = grammar_model.generate(**grammar_input, max_length=512)
+    corrected_text = grammar_tokenizer.decode(grammar_output[0], skip_special_tokens=True)
 
-    # 2. Summary (Concise)
-    summary_output = summarizer(corrected_text, max_length=120, min_length=30, do_sample=False)
-    summary_text = summary_output[0]['summary_text']
+    # --- 2. Summary Generation ---
+    summary_text = summarizer(corrected_text, max_length=120, min_length=40, do_sample=False)[0]['summary_text']
 
-    # 3. Title Generation
+    # --- 3. Title Generation ---
     title_prompt = "generate title: " + corrected_text
-    title_inputs = title_tokenizer.encode(title_prompt, return_tensors="pt", truncation=True, max_length=512)
-    title_outputs = title_model.generate(title_inputs, max_length=20, num_beams=5)
-    generated_title = title_tokenizer.decode(title_outputs[0], skip_special_tokens=True)
+    title_inputs = title_tokenizer(title_prompt, return_tensors="pt", max_length=512, truncation=True)
+    title_output = title_model.generate(**title_inputs, max_length=20, num_beams=5)
+    generated_title = title_tokenizer.decode(title_output[0], skip_special_tokens=True)
 
     return corrected_text, summary_text, generated_title
 
-# Gradio Interface
+# Gradio interface
 iface = gr.Interface(
     fn=polish_abstract,
-    inputs=gr.Textbox(lines=10, label="Paste Your Raw Abstract Here"),
+    inputs=gr.Textbox(lines=12, label="Paste Your Abstract"),
     outputs=[
-        gr.Textbox(label="✅ Corrected Abstract (Grammar Fixed)"),
-        gr.Textbox(label="🪄 Polished Summary (Concise Version)"),
-        gr.Textbox(label="📘 Suggested Title"),
+        gr.Textbox(label="✅ Grammar-Corrected Abstract"),
+        gr.Textbox(label="🪄 Concise Summary"),
+        gr.Textbox(label="📘 AI-Suggested Title"),
     ],
     title="🧠 AI Abstract & Title Polisher",
-    description="Paste your rough abstract. This AI tool will correct grammar, generate a concise summary, and suggest a scientific title."
+    description="This tool corrects grammar, summarizes, and suggests a title for biomedical abstracts."
 )
 
 iface.launch()