app.py

import gradio as gr
import os
from pathlib import Path
import argparse
from huggingface_hub import snapshot_download


# repo_name = "TheBloke/Mistral-7B-v0.1-GGUF"
# model_file = "mistral-7b-v0.1.Q6_K.gguf"

#repo_name = 'HumanityFTW/so_rude'
#model_file = "mistral-comedy-2.0-ckpt-600.Q6_K.gguf"

repo_name = 'TheBloke/OpenHermes-2.5-Mistral-7B-GGUF'
model_file = "openhermes-2.5-mistral-7b.Q4_K_M.gguf"

print('Fetching model:', repo_name, model_file)
snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_file)
print('Done fetching model:')

DEFAULT_MODEL_PATH = model_file

from llama_cpp import Llama
llm = Llama(model_path=model_file, model_type="mistral")


def predict(input, chatbot, max_length, top_p, temperature, history):
    chatbot.append((input, ""))
    response = ""
    history.append(input)

    for output in llm(input, stream=True, temperature=temperature, top_p=top_p, max_tokens=max_length, ):
        piece = output['choices'][0]['text']
        response += piece
        chatbot[-1] = (chatbot[-1][0], response)

        yield chatbot, history

    history.append(response)
    yield chatbot, history


def reset_user_input():
    return gr.update(value="")


def reset_state():
    return [], []


def AIPatient(message):

    global isFirstRun, history,context

    if isFirstRun:
      context = initContext
      isFirstRun = False
    #else:
      #for turn in history:
      #  context += f"\n<|im_start|> Nurse: {turn[0]}\n<|im_start|> Barry: {turn[1]}"
    context += """
                  <|im_start|>nurse
                  Nurse: """+message+"""
                  <|im_start|>barry
                  Barry:
                  """

    response = ""
    # Here, you should add the code to generate the response using your model
    # For example:
    while(len(response) < 1):
      output = llm(context, max_tokens=400, stop=["Nurse:"], echo=False)
      response = output["choices"][0]["text"]
      response = response.strip()

    with feedback_file.open("a") as f:
            f.write(json.dumps({"Nurse": message, "Barry": response},indent=4))
            f.write("\n")

    context += response
    print (context)

    history.append((message,response))
    return history

with gr.Blocks() as demo:
    gr.Markdown("# AI Patient Chatbot")
    with gr.Group():
        with gr.Tab("Patient Chatbot"):
            chatbot = gr.Chatbot()
            message = gr.Textbox(label="Enter your message to Barry", placeholder="Type here...", lines=2)
            send_message = gr.Button("Submit")
            send_message.click(AIPatient, inputs=[message], outputs=[chatbot])
            save_chatlog = gr.Button("Save Chatlog")
            #send_message.click(SaveChatlog, inputs=[message], outputs=[chatbot])