model use

app.py

@@ -1,7 +1,36 @@
 import gradio as gr
+from transformers import pipeline
+
+import re
+
+pipe = pipeline("summarization", model="kkasiviswanath/bart_summarizer_deploy_v1")
+
+def summarize_email(email_body, pipe):
+    # Tokenize the input text
+    input_tokens = pipe.tokenizer(email_body, return_tensors='pt', truncation=False)
+    input_length = input_tokens['input_ids'].shape[1]
+
+    # Adjust max_length to be a certain percentage of the input length
+    adjusted_max_length = max(3, int(input_length * 0.6))  # Ensure a minimum length
+    # Generate summary with dynamic max_length
+    gen_kwargs = {
+        "length_penalty": 2.0,
+        "num_beams": 5,
+        "max_length": adjusted_max_length,
+        "min_length": 3
+    }
+
+    summary = pipe(email_body, **gen_kwargs)[0]['summary_text']
+    return summary
+
+# Generate summaries for the test dataset
+def generate_summary(text):
+    email_body = re.sub(r'\s+', ' ', re.sub(r'[^\w\s]', '', text).strip())
+    summary = summarize_email(email_body, pipe)
+    return summary
 
 def greet(name):
     return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+demo = gr.Interface(fn=generate_summary, inputs="text", outputs="text")
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1 @@
+transformers