Update app.py

app.py

@@ -1,17 +1,28 @@
 import gradio as gr
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    AutoModelForSeq2SeqLM,
+)
+import torch
+model_folder = "zjunlp/chatcell-small"
+tokenizer = AutoTokenizer.from_pretrained(model_folder)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_folder)
+model.eval()
 
 
-def count_tokens(text):
-    return 0
-    # return len(TOKENIZER(text).input_ids)
 
+def run_detector(input_text):
+    # Encode the input text and generate a response with specified generation parameters
+    input_ids = tokenizer(input_text,return_tensors="pt").input_ids.to(device)
+    output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True)
+    # Decode and print the generated output text
+    output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True)
 
-def run_detector(input_str):
-    return 1
-    # if count_tokens(input_str) < MINIMUM_TOKENS:
-    #     gr.Warning(f"Too short length. Need minimum {MINIMUM_TOKENS} tokens to run Binoculars.")
-    #     return ""
-    # return f"{BINO.predict(input_str)}"
+    return output_ids
 
 
 
@@ -98,6 +109,8 @@ with gr.Blocks(css=css,
         ],
         examples_per_page=3,
         inputs=[input_box],
+        labels=["类别1", "类别2", "类别3", "类别4"]  # 这里是示例标签，您需要根据实际情况进行更改
+
     )
 
 
@@ -125,7 +138,7 @@ with gr.Blocks(css=css,
             """
         )
 
-    with gr.Accordion("Cite our work", open=False):
+    with gr.Accordion("📙Cite our work", open=False):
         gr.Markdown(
             """
             ```bibtex