Update app.py

app.py

@@ -1,117 +1,3 @@
-'''
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
-import gradio as gr
-import torch
-from peft import PeftConfig, PeftModel
-
-# Loading PEFT model
-PEFT_MODEL = "TurtleLiu/mistral7b_psychology_bot"
-
-config = PeftConfig.from_pretrained(PEFT_MODEL)
-
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit= True,
-    bnb_4bit_quant_type= "nf4",
-    bnb_4bit_compute_dtype= torch.bfloat16,
-    bnb_4bit_use_double_quant= False,
-)
-
-peft_base_model = AutoModelForCausalLM.from_pretrained(
-    config.base_model_name_or_path,
-    return_dict=True,
-    quantization_config=bnb_config,
-    device_map="auto",
-    trust_remote_code=True,
-)
-
-model = PeftModel.from_pretrained(peft_base_model, PEFT_MODEL)
-model = model.merge_and_unload()
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
-tokenizer.pad_token = tokenizer.eos_token
-tokenizer.padding_side = "right"
-
-# Generate response
-
-def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
-
-pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200, do_sample=True,
-    max_new_tokens=1024, 
-    temperature=0.9, 
-    top_k=50, 
-    top_p=0.95,
-    num_return_sequences=1)
-
-def generate_response(message, history):
-    prompt = "<s>"
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    result = pipe(f"{prompt}")[0]['generated_text']
-    return result
-
-'''
-'''
-def generate_response(prompt, history, temperature=0.9, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0, **kwargs,):
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-    runtimeFlag = "cuda:0"
-    formatted_prompt = format_prompt(f"{prompt}", history)
-    inputs = tokenizer([formatted_prompt], return_tensors="pt").to(runtimeFlag)
-    generation_config = GenerationConfig(
-        temperature=temperature,
-        top_p=top_p,
-        max_new_tokens=max_new_tokens,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        **kwargs,
-    )
-    generation_output = model.generate(
-            **inputs,
-            generation_config=generation_config,
-            return_dict_in_generate=True,
-            output_scores=True,
-            max_new_tokens=max_new_tokens,
-        )
-'''
-'''
-# UI design
-examples=[
-    ["Patient is feeling stressed due to work and has trouble sleeping.", None, None, None, None, None],
-    ["Client is dealing with relationship issues and is seeking advice on communication strategies.", None, None, None, None, None],
-    ["Individual has recently experienced a loss and is having difficulty coping with grief.", None, None, None, None, None],
-]
-
-gr.ChatInterface(
-    fn=generate_response,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    title="Psychological Assistant: Expert in Assessment and Strategic Planning",
-    description="Enter counseling notes to generate an assessment and plan.",
-    examples=examples,
-    concurrency_limit=20,
-).launch(show_api=False, debug=True)
-'''    
-
-
 from huggingface_hub import InferenceClient
 import gradio as gr