from huggingface_hub import InferenceClient import gradio as gr import json from datetime import datetime,timedelta import os from google_auth_oauthlib.flow import InstalledAppFlow from googleapiclient.discovery import build from google.oauth2 import service_account from googleapiclient.http import MediaFileUpload from pathlib import Path import argparse from huggingface_hub import snapshot_download import random import string import re from llama_cpp import Llama # Variables for model, Google Drive, and initial context repo_name = 'TheBloke/OpenHermes-2.5-Mistral-7B-GGUF' model_file = "openhermes-2.5-mistral-7b.Q4_K_M.gguf" SCOPES = ['https://www.googleapis.com/auth/drive'] SERVICE_ACCOUNT_FILE = '/home/user/app/service_account_creds.json' folder_id = '1Hjbu7FmuVs0Yz8y_veo6SzY_2tc48OWt' initContext = """<|im_start|>You are playing the role of an aggressive patient called Barry Lawson. Barry has been waiting for hours at the hospital with severe pain in his chest and hasn't been seen yet. You are incredibly frustrated and angry that things are taking so long, you will only calm down if your concerns are addressed. Waiting will only make you more angry and aggressive towards the staff. At any moment you could break into a RAGE! Make sure you don't censor swear words. Make sure to explain your emotional state and thinking before responding, for example, Barry: (tired of waiting and very angry) What the fuck do I have to do around here to get some treatment!""" unique_id = "" timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") # Function to load the LLaMA model def load_model(): llm = Llama(model_path=model_file, model_type="mistral",n_gpu_layers=100,n_ctx = 2048,verbose=True) return llm # Function to generate a unique identifier for each chat session def generate_unique_id(): # Generate a random sequence of 3 letters and 3 digits letters = ''.join(random.choices(string.ascii_letters, k=1)) digits = ''.join(random.choices(string.digits, k=2)) unique_id = letters + digits return unique_id # Download the model from Hugging Face Hub print('Fetching model:', repo_name, model_file) snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_file) print('Done fetching model:') class ChatbotAPP: def __init__(self,model,service_account_file,scopes,folder_id,unique_id,initContext): self.llm = model # LLaMA model instance self.service_account_file = service_account_file # Path to Google service account credentials self.scopes = scopes # Google Drive API scopes self.folder_id = folder_id # Google Drive folder ID to store chat logs self.unique_id = unique_id # Unique identifier for the chat session self.chat_history = [] # List to store chat history for the current session self.chat_log_history = [] # List to store chat logs for uploading self.isFirstRun = True # Flag to check if it's the first run of the chat session self.initContext = initContext # Initial context for the chat session self.context = "" # Current context for the chat session self.agreed = False # Flag to check if the user agreed to terms and conditions self.service = self.get_drive_service() # Google Drive service instance self.app = self.create_app() # Gradio app instance self.chat_log_name = "" # Filename for the chat log self.start_time=datetime.now() #save the chat session start # Method to create Google Drive service instance def get_drive_service(self): credentials = service_account.Credentials.from_service_account_file( self.service_account_file, scopes=self.scopes) self.service = build('drive', 'v3', credentials=credentials) print("Google Service Created") return self.service def generate_unique_id(self): #in an instance the user resets using the reset button # Generate a random sequence of 3 letters and 3 digits letters = ''.join(random.choices(string.ascii_letters, k=1)) digits = ''.join(random.choices(string.digits, k=2)) unique_id = letters + digits return unique_id # Method to search for a chat log file in Google Drive def search_file(self): #Search for a file by name in the specified Google Drive folder. query = f"name = '{self.chat_log_name}' and '{self.folder_id}' in parents and trashed = false" response = self.service.files().list(q=query, spaces='drive', fields='files(id, name)').execute() files = response.get('files', []) if not files: print(f"Chat log {self.chat_log_name} does not exist") else: print(f"Chat log {self.chat_log_name} exist") return files def strip_text(self, text): # Pattern to match text inside parentheses or angle brackets, any text following angle brackets, # new line characters, and anything after ',', '<', or '|' pattern = r"\(.*?\)|<.*?>.*|\n|\s{3,}" # Use re.sub() to replace the matched text with an empty string cleaned_text = re.sub(pattern, "", text) cleaned_text = cleaned_text.replace("im_start", "").replace("im_end", "").replace("<","").replace("|","").replace(":","") return cleaned_text def upload_to_google_drive(self): # Method to upload the current chat log to Google Drive existing_files = self.search_file() print(existing_files) data = { #"name": Name, #"occupation": Occupation, #"years of experience": YearsOfExp, #"ethnicity": Ethnicity, #"gender": Gender, #"age": Age, "Unique ID": self.unique_id, "chat_history": self.chat_log_history } with open(self.chat_log_name, "w") as log_file: json.dump(data, log_file, indent=4) if not existing_files: # Upload or update the chat log file on Google Drive # If the file does not exist, upload it file_metadata = { 'name': self.chat_log_name, 'parents': [self.folder_id],'mimeType': 'application/json' } media = MediaFileUpload(self.chat_log_name, mimetype='application/json') file = self.service.files().create(body=file_metadata, media_body=media, fields='id').execute() print(f"Uploaded new file with ID: {file.get('id')}") else: print(f"File '{self.chat_log_name}' already exists.") # Example: Update the file content file_id = existing_files[0]['id'] media = MediaFileUpload(self.chat_log_name, mimetype='application/json') updated_file = self.service.files().update(fileId=file_id, media_body=media).execute() print(f"Updated existing file with ID: {updated_file.get('id')}") def generate(self,prompt, history): # Method to generate a response to the user's input #if not len(Name) == 0 and not len(Occupation) == 0 and not len(Ethnicity) == 0 and not len(Gender) == 0 and not len(Age) == 0 and not len(YearsOfExp): if self.agreed: current_time = datetime.now() user_msg_timestamp = datetime.now().strftime("%H:%M:%S") #capture timestamp the user message is recieved. firstmsg ="" if self.isFirstRun: self.start_time = datetime.now() self.context = self.initContext self.isFirstRun = False firstmsg = prompt if (current_time - self.start_time) > timedelta (minutes = 10): temp_history = [] temp_history.append((""" . . . . . ""","10 minutes have been elapsed. Barry died due to not getting treatment!")) return temp_history self.context += """ <|im_start|>nurse Nurse:"""+prompt+""" <|im_start|>barry Barry: """ response = "" while(len(response) < 1): output = self.llm(self.context, max_tokens=400, stop=["Nurse:"], echo=False) response = output["choices"][0]["text"] response = response.strip() #yield response # for output in llm(input, stream=True, max_tokens=100, ): # piece = output['choices'][0]['text'] # response += piece # chatbot[-1] = (chatbot[-1][0], response) # yield response cleaned_response = self.strip_text(response) bot_msg_timestamp = datetime.now().strftime("%H:%M:%S") #Time stamp after the generating response. self.chat_history.append((prompt,cleaned_response)) if not self.isFirstRun: self.chat_log_history.append({"user": "("+user_msg_timestamp+")"+prompt, "bot": "("+bot_msg_timestamp+")"+cleaned_response}) self.upload_to_google_drive() else: self.chat_log_history.append({"user": firstmsg, "bot": cleaned_response}) self.context += response print (self.context) return self.chat_history else: temp_history=[] output = "Did you forget to Agree to the Terms and Conditions?" temp_history.append((prompt,output)) return temp_history def update_chatlog_name(self): self.chat_log_name = f'chat_log_for_{self.unique_id}_{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json' return self.chat_log_name def start_chat_button_fn(self,agree_status): # Method to handle the start chat button action if agree_status: self.agreed = agree_status self.update_chatlog_name() return f"You can start chatting now." else: return "You must agree to the terms and conditions to proceed" def reset_chat_interface(self): # Method to reset the chat interface self.chat_history = [] self.chat_log_history = [] self.isFirstRun = True return "Chat has been reset." def reset_name_interface(self): # Method to create the Gradio app interface Name = "" Occupation = "" YearsOfExp = "" Ethnicity = "" Gender = "" Age = "" chat_log_name = "" return "User info has been reset." def reset_all(self): message1 = self.reset_chat_interface() #message2 = reset_name_interface() #message3 = load_model() self.unique_id = self.generate_unique_id() self.update_chatlog_name() self.isFirstRun = True return f"All Chat components have been rest. Uniqe ID for this session is, {self.unique_id}. Please note this down.",self.unique_id def clear_chat_window(self): return [] def create_app(self): # Method to launch the Gradio app with gr.Blocks(theme=gr.themes.Soft()) as app: gr.Markdown("# ECU-IVADE: Chat With Barry") unique_id_display = gr.Textbox(value=self.unique_id, label="Session Unique ID", interactive=False,show_copy_button = True) clock_html = """
Current Time:
""" # Add the HTML component with the clock to the app gr.HTML(value=clock_html) with gr.Tab("Terms and Conditions"): #name = gr.Textbox(label="Name") #occupation = gr.Textbox(label="Occupation") #yearsofexp = gr.Textbox(label="Years of Experience") #ethnicity = gr.Textbox(label="Ethnicity") #gender = gr.Dropdown(choices=["Male", "Female", "Other", "Prefer Not To Say"], label="Gender") #age = gr.Textbox(label="Age") #submit_info = gr.Button("Submit") gr.Markdown("## Terms and Conditions") gr.Markdown(""" Before Talking with Barry, please read the following terms and conditions carefully: - **Data Collection**: Our chatbot collects chat logs for the purpose of improving our services and user experience. - **Privacy**: We ensure the confidentiality and security of your data, in line with our privacy policy. - **Survey**: At the end of the chat session, you will be asked to participate in a short survey to gather feedback about your experience. - **Consent**: By checking the box below and initiating the chat, you agree to these terms and the collection of chat logs, and consent to take part in the survey upon completing your session. Please check the box below to acknowledge your agreement and proceed. """) agree_status = gr.Checkbox(label="I have read and understand the terms and conditions.") status_label = gr.Markdown() start_chat_button = gr.Button("Agree to Terms and Conditions") #submit_info.click(submit_user_info, inputs=[name, occupation, yearsofexp, ethnicity, gender, age], outputs=[status_textbox]) start_chat_button.click(self.start_chat_button_fn, inputs=[agree_status], outputs=[status_label]) #status_textbox = gr.Textbox(interactive = False) with gr.Tab("Barry"): chatbot = gr.Chatbot(show_label=True,label="Chat With Barry") msg = gr.Textbox(label="Type your message to Barry") send = gr.Button("Send") clear = gr.Button("Clear Chat") send.click(self.generate, inputs=[msg], outputs=chatbot) clear.click(self.clear_chat_window, inputs=[], outputs=chatbot) with gr.Tab("Reset"): reset_button = gr.Button("Reset The Conversation With Barry") reset_output = gr.Textbox(label="Reset Output", interactive=False) reset_button.click(self.reset_all, inputs=[], outputs=[reset_output,unique_id_display]) return app # Create an instance of the ChatbotAPP class and launch the app llm = load_model() unique_id = generate_unique_id() chatbot_app = ChatbotAPP(llm,SERVICE_ACCOUNT_FILE,SCOPES,folder_id,unique_id,initContext) app = chatbot_app.create_app() app.launch(debug=True)