Update app.py

app.py

@@ -1,142 +0,0 @@
-# -*- coding: utf-8 -*-
-"""gradio_chatbot_app.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/#fileId=https%3A//huggingface.co/spaces/ZamiSanj/therapx/blob/main/gradio_chatbot_app.ipynb
-"""
-
-# !pip install gradio==3.41.0 transformers==4.32.0 langchain==0.0.273 -Uqqq
-# !pip install accelerate bitsandbytes==0.41.1 einops==0.7.0 peft==0.4.0 -Uqqq
-
-import gradio as gr
-import torch
-import re, os, warnings
-from langchain import PromptTemplate, LLMChain
-from langchain.llms.base import LLM
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig
-from peft import LoraConfig, get_peft_model, PeftConfig, PeftModel
-warnings.filterwarnings("ignore")
-
-# initialize and load PEFT model and tokenizer
-def init_model_and_tokenizer(PEFT_MODEL):
-  config = PeftConfig.from_pretrained(PEFT_MODEL)
-  bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_compute_dtype=torch.float16,
-  )
-
-  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,
-  )
-
-  peft_model = PeftModel.from_pretrained(peft_base_model, PEFT_MODEL)
-
-  peft_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-  peft_tokenizer.pad_token = peft_tokenizer.eos_token
-
-  return peft_model, peft_tokenizer
-
-# custom LLM chain to generate answer from PEFT model for each query
-def init_llm_chain(peft_model, peft_tokenizer):
-    class CustomLLM(LLM):
-        def _call(self, prompt: str, stop=None, run_manager=None) -> str:
-            device = "cuda:0"
-            peft_encoding = peft_tokenizer(prompt, return_tensors="pt").to(device)
-            peft_outputs = peft_model.generate(input_ids=peft_encoding.input_ids, generation_config=GenerationConfig(max_new_tokens=256, pad_token_id = peft_tokenizer.eos_token_id, \
-                                                                                                                     eos_token_id = peft_tokenizer.eos_token_id, attention_mask = peft_encoding.attention_mask, \
-                                                                                                                     temperature=0.4, top_p=0.6, repetition_penalty=1.3, num_return_sequences=1,))
-            peft_text_output = peft_tokenizer.decode(peft_outputs[0], skip_special_tokens=True)
-            return peft_text_output
-
-        @property
-        def _llm_type(self) -> str:
-            return "custom"
-
-    llm = CustomLLM()
-
-    template = """Answer the following question truthfully.
-    If you don't know the answer, respond 'Sorry, I don't know the answer to this question.'.
-    If the question is too complex, respond 'Kindly, consult a psychiatrist for further queries.'.
-
-    Example Format:
-    <HUMAN>: question here
-    <ASSISTANT>: answer here
-
-    Begin!
-
-    <HUMAN>: {query}
-    <ASSISTANT>:"""
-
-    prompt = PromptTemplate(template=template, input_variables=["query"])
-    llm_chain = LLMChain(prompt=prompt, llm=llm)
-
-    return llm_chain
-
-def user(user_message, history):
-  return "", history + [[user_message, None]]
-
-def bot(history):
-  if len(history) >= 2:
-    query = history[-2][0] + "\n" + history[-2][1] + "\nHere, is the next QUESTION: " + history[-1][0]
-  else:
-    query = history[-1][0]
-
-  bot_message = llm_chain.run(query)
-  bot_message = post_process_chat(bot_message)
-
-  history[-1][1] = ""
-  history[-1][1] += bot_message
-  return history
-
-def post_process_chat(bot_message):
-  try:
-    bot_message = re.findall(r"<ASSISTANT>:.*?Begin!", bot_message, re.DOTALL)[1]
-  except IndexError:
-    pass
-
-  bot_message = re.split(r'<ASSISTANT>\:?\s?', bot_message)[-1].split("Begin!")[0]
-
-  bot_message = re.sub(r"^(.*?\.)(?=\n|$)", r"\1", bot_message, flags=re.DOTALL)
-  try:
-    bot_message = re.search(r"(.*\.)", bot_message, re.DOTALL).group(1)
-  except AttributeError:
-    pass
-
-  bot_message = re.sub(r"\n\d.$", "", bot_message)
-  bot_message = re.split(r"(Goodbye|Take care|Best Wishes)", bot_message, flags=re.IGNORECASE)[0].strip()
-  bot_message = bot_message.replace("\n\n", "\n")
-
-  return bot_message
-
-model = "heliosbrahma/falcon-7b-sharded-bf16-finetuned-mental-health-conversational"
-peft_model, peft_tokenizer = init_model_and_tokenizer(PEFT_MODEL = model)
-
-with gr.Blocks() as demo:
-    gr.HTML("""<h1>TherapX</h1>""")
-    gr.Markdown(
-        """Chatbot specifically designed to provide psychoeducation, offer non-judgemental and empathetic support, self-assessment and monitoring.<br>
-        Get instant response for any mental health related queries. If the chatbot seems you need external support, then it will respond appropriately.<br>"""
-    )
-
-    chatbot = gr.Chatbot()
-    query = gr.Textbox(label="Type your query here, then press 'enter' and scroll up for response")
-    clear = gr.Button(value="Clear Chat History!")
-    clear.style(size="sm")
-
-    llm_chain = init_llm_chain(peft_model, peft_tokenizer)
-
-    query.submit(user, [query, chatbot], [query, chatbot], queue=False).then(bot, chatbot, chatbot)
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.queue().launch()
-
-# !gradio deploy
-