change the app

app.py

@@ -1,79 +1,77 @@
+import torch
 import gradio as gr
-from huggingface_hub import InferenceClient
-from typing import List, Dict
-
-# Response function for the chatbot
-def respond(
-    message: str,
-    history: List[Dict[str, str]],
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    hf_token: gr.OAuthToken,
-):
-    """
-    Sends a user input to the summarization model using text-to-text interface.
-    """
-    client = InferenceClient(
-        token=hf_token.token,
-        model="Bocklitz-Lab/lit2vec-tldr-bart-model"
-    )
-
-    # You can prepend the system message if needed
-    input_text = f"{system_message}\n\n{message}"
-
-    response = client.text_to_text(
-        input=input_text,
-        max_new_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p
-    )
-
-    yield response
-
-
-# Define the Gradio interface
-chatbot = gr.ChatInterface(
-    fn=respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(
-            value="You are a friendly chatbot.",
-            label="System message",
-            lines=1
-        ),
-        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)"
-        ),
-    ],
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
+
+# List of summarization models
+model_names = [
+    "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."
 )
 
-# Set up the full Gradio Blocks layout with login
+# 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.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)}"
+
+# Function to summarize the input text
+def summarize_text(input, min_length, max_length):
+    if summarizer is None:
+        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:
+        return f"Summarization failed: {str(e)}"
+
+# Gradio Interface
 with gr.Blocks() as demo:
     with gr.Row():
-        with gr.Column(scale=1):
-            gr.LoginButton()
-    chatbot.render()
+        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)
+
+    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)
+
+    input_text = gr.Textbox(label="Input text to summarize", lines=6, value=example_text)
+    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)
 
-# Run the app
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1,4 +1,5 @@
 transformers
+torch
 onnxruntime
 gradio==4.19
 huggingface_hub==0.22.2