remove model selector

app.py

@@ -2,36 +2,31 @@ import torch
 import gradio as gr
 from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
 
-# List of summarization models
-model_names = [
-    "Bocklitz-Lab/lit2vec-tldr-bart-model"
-]
+# Model name
+model_name = "Bocklitz-Lab/lit2vec-tldr-bart-model"
 
 # Placeholder for the summarizer pipeline, tokenizer, and maximum tokens
 summarizer = None
 tokenizer = None
 max_tokens = None
 
-# Example text for summarization
-example_text = (
-    "Ultraviolet B (UVB; 290~320nm) irradiation-induced lipid peroxidation induces inflammatory responses that lead to skin wrinkle formation and epidermal thickening. Peroxisome proliferator-activated receptor (PPAR) α/γ dual agonists have the potential to be used as anti-wrinkle agents because they inhibit inflammatory response and lipid peroxidation. In this study, we evaluated the function of 2-bromo-4-(5-chloro-benzo[d]thiazol-2-yl) phenol (MHY 966), a novel synthetic PPAR α/γ dual agonist, and investigated its anti-inflammatory and anti-lipid peroxidation effects. The action of MHY 966 as a PPAR α/γ dual agonist was also determined in vitro by reporter gene assay. Additionally, 8-week-old melanin-possessing hairless mice 2 (HRM2) were exposed to 150 mJ/cm2 UVB every other day for 17 days and MHY 966 was simultaneously pre-treated every day for 17 days to investigate the molecular mechanisms involved. MHY 966 was found to stimulate the transcriptional activities of both PPAR α and γ. In HRM2 mice, we found that the skins of mice exposed to UVB showed significantly increased pro-inflammatory mediator levels (NF-κB, iNOS, and COX-2) and increased lipid peroxidation, whereas MHY 966 co-treatment down-regulated these effects of UVB by activating PPAR α and γ. Thus, the present study shows that MHY 966 exhibits beneficial effects on inflammatory responses and lipid peroxidation by simultaneously activating PPAR α and γ. The major finding of this study is that MHY 966 demonstrates potential as an agent against wrinkle formation associated with chronic UVB exposure."
-)
-
-# Function to load the selected model
-def load_model(model_name):
+# Automatically load the model at startup
+def initialize_model():
     global summarizer, tokenizer, max_tokens
     try:
-        # Load the summarization pipeline with the selected model
         summarizer = pipeline("summarization", model=model_name, torch_dtype=torch.float32)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         config = AutoConfig.from_pretrained(model_name)
-
-        # Set a reasonable default for max_tokens if not available
         max_tokens = getattr(config, 'max_position_embeddings', 1024)
-
-        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)}"
+        print(f"Model loading failed: {str(e)}")
+
+initialize_model()  # Load model at startup
+
+# Example input
+example_text = (
+    "Ultraviolet B (UVB; 290~320nm) irradiation-induced lipid peroxidation induces inflammatory responses that lead to skin wrinkle formation and epidermal thickening. Peroxisome proliferator-activated receptor (PPAR) α/γ dual agonists have the potential to be used as anti-wrinkle agents because they inhibit inflammatory response and lipid peroxidation. In this study, we evaluated the function of 2-bromo-4-(5-chloro-benzo[d]thiazol-2-yl) phenol (MHY 966), a novel synthetic PPAR α/γ dual agonist, and investigated its anti-inflammatory and anti-lipid peroxidation effects. The action of MHY 966 as a PPAR α/γ dual agonist was also determined in vitro by reporter gene assay. Additionally, 8-week-old melanin-possessing hairless mice 2 (HRM2) were exposed to 150 mJ/cm2 UVB every other day for 17 days and MHY 966 was simultaneously pre-treated every day for 17 days to investigate the molecular mechanisms involved. MHY 966 was found to stimulate the transcriptional activities of both PPAR α and γ. In HRM2 mice, we found that the skins of mice exposed to UVB showed significantly increased pro-inflammatory mediator levels (NF-κB, iNOS, and COX-2) and increased lipid peroxidation, whereas MHY 966 co-treatment down-regulated these effects of UVB by activating PPAR α and γ. Thus, the present study shows that MHY 966 exhibits beneficial effects on inflammatory responses and lipid peroxidation by simultaneously activating PPAR α and γ. The major finding of this study is that MHY 966 demonstrates potential as an agent against wrinkle formation associated with chronic UVB exposure."
+)
 
 # Function to summarize the input text
 def summarize_text(input, min_length, max_length):
@@ -39,17 +34,15 @@ def summarize_text(input, min_length, max_length):
         return "No model loaded!"
 
     try:
-        # 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 f"Error: Input exceeds the max token limit of {max_tokens}."
 
-        # Ensure min/max lengths are within bounds
         min_summary_length = max(10, int(num_tokens * (min_length / 100)))
         max_summary_length = min(max_tokens, int(num_tokens * (max_length / 100)))
 
-        # Summarize the input text
         output = summarizer(input, min_length=min_summary_length, max_length=max_summary_length, truncation=True)
         return output[0]['summary_text']
     except Exception as e:
@@ -57,11 +50,7 @@ def summarize_text(input, min_length, max_length):
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    with gr.Row():
-        model_dropdown = gr.Dropdown(choices=model_names, label="Choose a model", value="sshleifer/distilbart-cnn-12-6")
-        load_button = gr.Button("Load Model")
-
-    load_message = gr.Textbox(label="Load Status", interactive=False)
+    gr.Markdown("## TL;DR Summarizer")
 
     min_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Minimum Summary Length (%)", value=10)
     max_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Maximum Summary Length (%)", value=20)
@@ -70,7 +59,6 @@ with gr.Blocks() as demo:
     summarize_button = gr.Button("Summarize Text")
     output_text = gr.Textbox(label="Summarized text", lines=4)
 
-    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)