Update app.py

app.py

@@ -1,13 +1,26 @@
 import gradio as gr
-from transformers import pipeline
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+from peft import PeftModel
 
-MODEL_ID = 'lityops/Abstractive-Style-Summarizer'
+ADAPTER_HUB = "lityops/Abstractive-Style-Summarizer"
+BASE_MODEL_NAME = "google/flan-t5-base"
 
-summarizer = pipeline("summarization", model=MODEL_ID)
+base_model = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL)
+model = PeftModel.from_pretrained(base_model, ADAPTER_HUB)
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_HUB)
+
+summarizer = pipeline(
+  "summarization", 
+  model=model, 
+  tokenizer=tokenizer
+)
 
 def generate_summary(text, style):
-  if not text or len(text.strip()) < 50:
-    return "Input must at least be 50 words long"
+  if not text or len(text.split()) < 100:
+    return "Input must at least be 100 words long"
+  if len(text.split()) > 512:
+    return "Input must at most be 512 words long"
 
   input_text = f"Summarize {style}: {text}"
   input_words = len(text.split())
@@ -48,6 +61,12 @@ def generate_summary(text, style):
   )
   return output[0]["summary_text"]
 
+custom_css = """
+#header {text-align: center; margin-bottom: 25px;}
+.gradio-container {max-width: 1000px !important;}
+footer {display: none !important;}
+"""
+
 custom_css = """
 #header {text-align: center; margin-bottom: 25px;}
 .gradio-container {max-width: 95% !important;}
@@ -73,7 +92,7 @@ with gr.Blocks() as demo:
       )
       with gr.Row():
         clear_btn = gr.Button("Clear Input")
-        submit_btn = gr.Button("Process Summary", variant="primary")
+        submit_btn = gr.Button("Generate Summary", variant="primary")
       
     with gr.Column(scale=1):
       output_box = gr.Textbox(