Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
+
+# List of summarization models
+model_names = [
+    "google/bigbird-pegasus-large-arxiv",
+    "facebook/bart-large-cnn",
+    "google/t5-v1_1-large",
+    "sshleifer/distilbart-cnn-12-6",
+    "allenai/led-base-16384",
+    "google/pegasus-xsum",
+    "togethercomputer/LLaMA-2-7B-32K"
+]
+
+# Placeholder for the summarizer pipeline, tokenizer, and maximum tokens
+summarizer = None
+tokenizer = None
+max_tokens = None
+
+
+# Function to load the selected model
+def load_model(model_name):
+    global summarizer, tokenizer, max_tokens
+    try:
+        # Load the summarization pipeline with the selected model
+        summarizer = pipeline("summarization", model=model_name, torch_dtype=torch.bfloat16)
+        # Load the tokenizer for the selected model
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        # Load the configuration for the selected model
+        config = AutoConfig.from_pretrained(model_name)
+
+        # Determine the maximum tokens based on available configuration attributes
+        if hasattr(config, 'max_position_embeddings'):
+            max_tokens = config.max_position_embeddings
+        elif hasattr(config, 'n_positions'):
+            max_tokens = config.n_positions
+        elif hasattr(config, 'd_model'):
+            max_tokens = config.d_model  # for T5 models, d_model is a rough proxy
+        else:
+            max_tokens = "Unknown"
+
+        return f"Model {model_name} loaded successfully! Max tokens: {max_tokens}"
+    except Exception as e:
+        return f"Failed to load model {model_name}. Error: {str(e)}"
+
+
+# Function to summarize the input text
+def summarize_text(input, min_length, max_length):
+    if summarizer is None:
+        return "No model loaded!"
+
+    # Tokenize the input text and check the number of tokens
+    input_tokens = tokenizer.encode(input, return_tensors="pt")
+    num_tokens = input_tokens.shape[1]
+    if num_tokens > max_tokens:
+        # Return an error message if the input text exceeds the maximum token limit
+        return f"Error: The input text has {num_tokens} tokens, which exceeds the maximum allowed {max_tokens} tokens. Please enter shorter text."
+
+    # Calculate minimum and maximum summary length based on the percentages
+    min_summary_length = int(num_tokens * (min_length / 100))
+    max_summary_length = int(num_tokens * (max_length / 100))
+
+    # Summarize the input text using the loaded model with specified lengths
+    output = summarizer(input, min_length=min_summary_length, max_length=max_summary_length)
+    return output[0]['summary_text']
+
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        # Dropdown menu for selecting the model
+        model_dropdown = gr.Dropdown(choices=model_names, label="Choose a model", value="sshleifer/distilbart-cnn-12-6")
+        # Button to load the selected model
+        load_button = gr.Button("Load Model")
+
+    # Textbox to display the load status
+    load_message = gr.Textbox(label="Load Status", interactive=False)
+
+    # Slider for minimum summary length
+    min_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Minimum Summary Length (%)", value=10)
+    # Slider for maximum summary length
+    max_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Maximum Summary Length (%)", value=20)
+
+    # Textbox for inputting the text to be summarized
+    input_text = gr.Textbox(label="Input text to summarize", lines=6)
+    # Button to trigger the summarization
+    summarize_button = gr.Button("Summarize Text")
+    # Textbox to display the summarized text
+    output_text = gr.Textbox(label="Summarized text", lines=4)
+
+    # Define the actions for the load button and summarize button
+    load_button.click(fn=load_model, inputs=model_dropdown, outputs=load_message)
+    summarize_button.click(fn=summarize_text, inputs=[input_text, min_length_slider, max_length_slider],
+                           outputs=output_text)
+
+# Launch the Gradio interface
+demo.launch()