Update space

app.py

@@ -1,63 +1,43 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import gradio as gr
+from transformers import pipeline
+
+# Load the summarization pipeline with your pre-trained model
+pipe = pipeline("summarization", model="paramasivan27/bart_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

@@ -1 +1,4 @@
-huggingface_hub==0.22.2
+huggingface_hub==0.22.2
+transformers
+torch
+gradio