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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import re
from gtts import gTTS
import os
import logging
from pyngrok import ngrok
# Set up logging
logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Function to set up the model and tokenizer
def setup_model(model_name):
logging.info('Setting up model and tokenizer.')
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=False,
revision="main"
)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model.eval()
logging.info('Model and tokenizer setup completed.')
return model, tokenizer
# Function to generate a response from the model
def generate_response(model, tokenizer, prompt, max_new_tokens=140):
logging.info('Generating response for the prompt.')
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=max_new_tokens)
response = tokenizer.batch_decode(outputs)[0]
# Extract only the response part (assuming everything after the last newline belongs to the response)
response_parts = response.split("\n")
logging.info('Response generated.')
return response_parts[-1] # Return the last element (response)
# Function to remove various tags using regular expressions
def remove_tags(text):
logging.info('Removing tags from the text.')
# Combine multiple tag removal patterns for broader coverage
tag_regex = r"<[^>]*>" # Standard HTML tags
custom_tag_regex = r"<.*?>|\[.*?\]|{\s*?\(.*?\)\s*}" # Custom, non-standard tags (may need adjustments)
all_tags_regex = f"{tag_regex}|{custom_tag_regex}" # Combine patterns
cleaned_text = re.sub(all_tags_regex, "", text)
logging.info('Tags removed.')
return cleaned_text
# Function to generate the audio file
def text_to_speech(text, filename="response.mp3"):
logging.info('Generating speech audio file.')
tts = gTTS(text)
tts.save(filename)
logging.info('Speech audio file saved.')
return filename
# Main function for the Gradio app
def main(comment):
logging.info('Main function triggered.')
instructions_string = (
"virtual marketer assistant, communicates in business, focused on services, "
"escalating to technical depth upon request. It reacts to feedback aptly and ends responses "
"with its signature Mr.jon will tailor the length of its responses to match the individual's comment, "
"providing concise acknowledgments to brief expressions of gratitude or feedback, thus keeping the interaction natural and supportive.\n"
)
model_name = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
try:
model, tokenizer = setup_model(model_name)
if comment:
prompt_template = lambda comment: f"[INST] {instructions_string} \n{comment} \n[/INST]"
prompt = prompt_template(comment)
response = generate_response(model, tokenizer, prompt)
# Apply tag removal before displaying the response
response_without_tags = remove_tags(response)
# Remove the "[/INST]" string at the end (assuming it's always present)
response_without_inst = response_without_tags.rstrip("[/INST]")
# Generate and return the response and the audio file
audio_file = text_to_speech(response_without_inst)
logging.info('Response and audio file generated.')
return response_without_inst, audio_file
else:
logging.warning('No comment entered.')
return "Please enter a comment to generate a response.", None
except Exception as e:
logging.error(f'Error occurred: {str(e)}')
return "An error occurred. Please try again later.", None
iface = gr.Interface(
fn=main,
inputs=gr.Textbox(lines=2, placeholder="Enter a comment..."),
outputs=["text", "file"],
title="Virtual Marketer Assistant",
description="Enter a comment and get a response from the virtual marketer assistant. Download the response as an MP3 file."
)
# Create a tunnel
public_url = ngrok.connect(7860)
print("Public URL:", public_url)
if __name__ == "__main__":
iface.launch(share=True)
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