Update app.py

app.py

@@ -19,7 +19,7 @@ user_message = "Allie kept track of how many kilometers she walked during the pa
 model_name = 'TIGER-Lab/StructLM-7B'
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-model.generation_config = GenerationConfig.from_pretrained(model_name)
+# model.generation_config = GenerationConfig.from_pretrained(model_name)
 # model.generation_config.pad_token_id = model.generation_config.eos_token_id
 
 @spaces.GPU
@@ -27,7 +27,6 @@ def predict(user_message, system_message="", assistant_message = "", tabular_dat
     prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n{assistant_message}\n\n{tabular_data}\n\n\nQuestion:\n\n{user_message}[/INST]"
     inputs = tokenizer(prompt, return_tensors='pt', add_special_tokens=True)
     input_ids = inputs["input_ids"].to(model.device)
-
     output_ids = model.generate(
         input_ids,
         max_length=input_ids.shape[1] + max_new_tokens,
@@ -37,7 +36,6 @@ def predict(user_message, system_message="", assistant_message = "", tabular_dat
         pad_token_id=tokenizer.eos_token_id,
         do_sample=do_sample
     )
-
     response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
     return response
 
@@ -58,7 +56,7 @@ def main():
                 repetition_penalty = gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0)
                 do_sample = gr.Checkbox(label="Do sample", value=False)
 
-        output_text = gr.Textbox(label="🐯📏TigerAI-StructLM-7B", interactive=True)
+        output_text = gr.Textbox(label="🐯📏TigerAI-StructLM-7B")
 
         gr.Button("Try🐯📏TigerAI-StructLM").click(
             predict,