Upload 2 files

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+gradio
+torch

use_model.py

@@ -0,0 +1,34 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
+import gradio as gr
+
+# Load model
+tokenizer = AutoTokenizer.from_pretrained('/content/drive/MyDrive/TextSummarizer2/model_directory')
+model = AutoModelForSeq2SeqLM.from_pretrained('/content/drive/MyDrive/TextSummarizer2/model_directory')
+tokenizer.model_max_length = 1024
+
+# Config
+gen_config = GenerationConfig(
+    max_length=150,
+    min_length=40,
+    length_penalty=2.0,
+    num_beams=4,
+    early_stopping=True
+)
+
+# Summarization function
+def summarize(blog_post):
+    input = tokenizer(blog_post, max_length=1024, truncation=True, return_tensors='pt')
+    summary_ids = model.generate(input['input_ids'], generation_config=gen_config)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Gradio UI
+iface = gr.Interface(
+    fn=summarize,
+    inputs=gr.Textbox(lines=15, label="Enter Text to Summarize"),
+    outputs=gr.Textbox(label="Summary"),
+    title="Text Summarizer",
+    description="Enter a long paragraph or blog post to get a summarized version."
+)
+
+iface.launch(share=True)