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app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import gradio as gr
+from transformers import pipeline
+
+# Load the summarization pipeline with your pre-trained model
+pipe = pipeline("summarization", model="paramasivan27/t5small_for_email_summarization_enron")
+
+# Function to summarize email
+def summarize_email(email_body):
+    # Tokenize the input text
+    pipeline = pipe
+    input_tokens = pipeline.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(10, int(input_length * 0.6))  # Ensure a minimum length
+
+    # Generate summary with dynamic max_length
+    gen_kwargs = {
+        "length_penalty": 2.0,
+        "num_beams": 4,
+        "max_length": adjusted_max_length,
+        "min_length": 3
+    }
+
+    summary = pipeline(email_body, **gen_kwargs)[0]['summary_text']
+    return summary
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=summarize_email,
+    inputs=gr.Textbox(lines=10, placeholder="Enter the email body here..."),
+    outputs="text",
+    title="Email Subject Line Generator",
+    description="Generate a subject line from an email body using GPT-2."
+)
+
+# Launch the interface
+iface.launch()
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+huggingface_hub==0.22.2
+transformers
+torch
+gradio