app.py

# import gradio as gr
# import torch
# import transformers
# from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# from peft import PeftConfig, PeftModel
# import warnings
# from threading import Thread

# warnings.filterwarnings("ignore")

# PEFT_MODEL = "givyboy/phi-2-finetuned-mental-health-conversational"

# SYSTEM_PROMPT = """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.'."""

# USER_PROMPT = lambda x: f"""<HUMAN>: {x}\n<ASSISTANT>: """
# ADD_RESPONSE = lambda x, y: f"""<HUMAN>: {x}\n<ASSISTANT>: {y}"""
# DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# bnb_config = BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_quant_type="nf4",
#     bnb_4bit_use_double_quant=True,
#     bnb_4bit_compute_dtype=torch.float16,
# )

# config = PeftConfig.from_pretrained(PEFT_MODEL)

# 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,
#     offload_folder="offload/",
#     offload_state_dict=True,
# )

# peft_model = PeftModel.from_pretrained(
#     peft_base_model,
#     PEFT_MODEL,
#     offload_folder="offload/",
#     offload_state_dict=True,
# )
# peft_model = peft_model.to(DEVICE)

# peft_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# peft_tokenizer.pad_token = peft_tokenizer.eos_token

# pipeline = transformers.pipeline(
#     "text-generation",
#     model=peft_model,
#     tokenizer=peft_tokenizer,
#     torch_dtype=torch.bfloat16,
#     trust_remote_code=True,
#     device_map="auto",
# )


# # def format_message(message: str, history: list[str], memory_limit: int = 3) -> str:
# #     if len(history) > memory_limit:
# #         history = history[-memory_limit:]

# #     if len(history) == 0:
# #         return f"{SYSTEM_PROMPT}\n{USER_PROMPT(message)}"

# #     formatted_message = f"{SYSTEM_PROMPT}\n{ADD_RESPONSE(history[0][0], history[0][1])}"

# #     for msg, ans in history[1:]:
# #         formatted_message += f"\n{ADD_RESPONSE(msg, ans)}"

# #     formatted_message += f"\n{USER_PROMPT(message)}"
# #     return formatted_message


# # def get_model_response(message: str, history: list[str]) -> str:
# #     formatted_message = format_message(message, history)
# #     sequences = pipeline(
# #         formatted_message,
# #         do_sample=True,
# #         top_k=10,
# #         num_return_sequences=1,
# #         eos_token_id=peft_tokenizer.eos_token_id,
# #         max_length=600,
# #     )[0]
# #     print(sequences["generated_text"])
# #     output = sequences["generated_text"].split("<ASSISTANT>:")[-1].strip()
# #     # print(f"Response: {output}")
# #     return output


# start_message = ""


# def user(message, history):
#     # Append the user's message to the conversation history
#     return "", history + [[message, ""]]


# def chat(message, history):
#     chat_history = []
#     for item in history:
#         chat_history.append({"role": "user", "content": item[0]})
#         if item[1] is not None:
#             chat_history.append({"role": "assistant", "content": item[1]})

#     message = f"{SYSTEM_PROMPT}\n{USER_PROMPT(message)}"
#     chat_history.append({"role": "user", "content": message})
#     messages = peft_tokenizer.apply_chat_template(chat_history, tokenize=False, add_generation_prompt=True)

#     # Tokenize the messages string
#     model_inputs = peft_tokenizer([messages], return_tensors="pt").to(DEVICE)
#     streamer = transformers.TextIteratorStreamer(
#         peft_tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
#     )
#     generate_kwargs = dict(
#         model_inputs,
#         streamer=streamer,
#         max_new_tokens=1024,
#         do_sample=True,
#         top_p=0.95,
#         top_k=1000,
#         temperature=0.75,
#         num_beams=1,
#     )
#     t = Thread(target=peft_model.generate, kwargs=generate_kwargs)
#     t.start()

#     # Initialize an empty string to store the generated text
#     partial_text = ""
#     for new_text in streamer:
#         # print(new_text)
#         partial_text += new_text
#         # Yield an empty string to cleanup the message textbox and the updated conversation history
#         yield partial_text


# chat = gr.ChatInterface(fn=chat, title="Mental Health Chatbot - by Jayda Hunte")
# chat.launch(share=True)

import os
from openai import OpenAI
from dotenv import load_dotenv
import gradio as gr

load_dotenv()
API_KEY = os.getenv("OPENAI_API_KEY")
openai = OpenAI(api_key=API_KEY)

create_msg = lambda x, y: {"role": x, "content": y}

SYSTEM_PROMPT = create_msg(
    "system",
    """You are a helpful mental health chatbot, please answer with care. 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.'.""".strip(),
)


def predict(message, history):
    history_openai_format = []
    history_openai_format.append(SYSTEM_PROMPT)
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human})
        history_openai_format.append({"role": "assistant", "content": assistant})
    history_openai_format.append({"role": "user", "content": message})

    response = openai.chat.completions.create(
        model="ft:gpt-3.5-turbo-0613:personal::8kBTG8eh", messages=history_openai_format, temperature=0.35, stream=True
    )

    partial_message = ""
    for chunk in response:
        if chunk.choices[0].delta.content is not None:
            partial_message = partial_message + chunk.choices[0].delta.content
            yield partial_message


gr.ChatInterface(fn=predict, title="Mental Health Chatbot").launch(share=True)