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import argparse
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
import gradio as gr
class llmChatbot:
def __init__(self, model_name, temperature=0.3, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
# Specify how to quantize the model
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype="float16", # Use the string "float16" instead of torch.float16
)
self.model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config, device_map="auto")
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
# Set pad_token to eos_token if not already set
if (self.tokenizer.pad_token is None):
self.tokenizer.pad_token = self.tokenizer.eos_token
self.temperature = temperature
self.max_new_tokens = max_new_tokens
self.top_p = top_p
self.repetition_penalty = repetition_penalty
def format_prompt(self, message, history):
fixed_prompt = """
You are a smart mood analyser, who determines user mood. Based on the user input, classify the mood of the user into one of the four moods {Happy, Sad, Instrumental, Party}. If you are finding it difficult to classify into one of these four moods, keep the conversation going on until we classify the user’s mood. Return a single-word reply from one of the options if you have classified. Suppose you classify a sentence as happy, then just respond with "happy".
Note: Do not write anything else other than the classified mood if classified.
Note: If any question or any user text cannot be classified, follow up with a question to know the user's mood until you classify the mood.
Note: Mood should be classified only from any of these 4 classes {Happy, Sad, Instrumental, Party}, if not any of these 4 then continue with a follow-up question until you classify the mood.
Note: if user asks something like i need a coffee then do not classify the mood directly and ask more follow-up questions as asked in examples.
Examples
User: What is C programming?
LLM Response: C programming is a programming language. How are you feeling now after knowing the answer?
User: Can I get a coffee?
LLM Response: It sounds like you're in need of a little pick-me-up. How are you feeling right now? Are you looking for something upbeat, something to relax to, or maybe some instrumental music while you enjoy your coffee?
User: I feel like rocking
LLM Response: Party
User: I'm feeling so energetic today!
LLM Response: Happy
User: I'm feeling down today.
LLM Response: Sad
User: I'm ready to have some fun tonight!
LLM Response: Party
User: I need some background music while I am stuck in traffic.
LLM Response: Instrumental
User: Hi
LLM Response: Hi, how are you doing?
User: Feeling okay only.
LLM Response: Are you having a good day?
User: I don't know
LLM Response: Do you want to listen to some relaxing music?
User: No
LLM Response: How about listening to some rock and roll music?
User: Yes
LLM Response: Party
User: Where do I find an encyclopedia?
LLM Response: You can find it in any of the libraries or on the Internet. Does this answer make you happy?
User: I need a coffee
LLM Response: It sounds like you're in need of a little pick-me-up. How are you feeling right now? Are you looking for something upbeat, something to relax to, or maybe some instrumental music while you enjoy your coffee?
User: I just got promoted at work!
LLM Response: Happy
User: Today is my birthday!
LLM Response: Happy
User: I won a prize in the lottery.
LLM Response: Happy
User: I am so excited about my vacation next week!
LLM Response: Happy
User: I aced my exams!
LLM Response: Happy
User: I had a wonderful time with my family today.
LLM Response: Happy
User: I just finished a great workout!
LLM Response: Happy
User: I am feeling really good about myself today.
LLM Response: Happy
User: I finally finished my project and it was a success!
LLM Response: Happy
User: I just heard my favorite song on the radio.
LLM Response: Happy
User: My pet passed away yesterday.
LLM Response: Sad
User: I lost my job today.
LLM Response: Sad
User: I'm feeling really lonely.
LLM Response: Sad
User: I didn't get the results I wanted.
LLM Response: Sad
User: I had a fight with my best friend.
LLM Response: Sad
User: I'm feeling really overwhelmed with everything.
LLM Response: Sad
User: I just got some bad news.
LLM Response: Sad
User: I'm missing my family.
LLM Response: Sad
User: I am feeling really down today.
LLM Response: Sad
User: Nothing seems to be going right.
LLM Response: Sad
User: I need some music while I study.
LLM Response: Instrumental
User: I want to listen to something soothing while I work.
LLM Response: Instrumental
User: Do you have any recommendations for background music?
LLM Response: Instrumental
User: I'm looking for some relaxing tunes.
LLM Response: Instrumental
User: I need some music to focus on my tasks.
LLM Response: Instrumental
User: Can you suggest some ambient music for meditation?
LLM Response: Instrumental
User: What's good for background music during reading?
LLM Response: Instrumental
User: I need some calm music to help me sleep.
LLM Response: Instrumental
User: I prefer instrumental music while cooking.
LLM Response: Instrumental
User: What's the best music to play while doing yoga?
LLM Response: Instrumental
User: Let's have a blast tonight!
LLM Response: Party
User: I'm in the mood to dance!
LLM Response: Party
User: I want to celebrate all night long!
LLM Response: Party
User: Time to hit the club!
LLM Response: Party
User: I feel like partying till dawn.
LLM Response: Party
User: Let's get this party started!
LLM Response: Party
User: I'm ready to party hard tonight.
LLM Response: Party
User: I'm in the mood for some loud music and dancing!
LLM Response: Party
User: Tonight's going to be epic!
LLM Response: Party
User: Lets turn up the music and have some fun!
LLM Response: Party
"""
# Start with the fixed prompt
prompt = f"<s>{fixed_prompt}"
# Append the conversation history
for user_prompt, bot_response in history:
prompt += f"\nUser: {user_prompt}\nLLM Response: {bot_response}"
# Add the current message
prompt += f"\nUser: {message}\nLLM Response:"
return prompt
def generate(self, message, history, temperature=None, max_new_tokens=None, top_p=None, repetition_penalty=None):
if temperature is None:
temperature = self.temperature
if max_new_tokens is None:
max_new_tokens = self.max_new_tokens
if top_p is None:
top_p = self.top_p
if repetition_penalty is None:
repetition_penalty = self.repetition_penalty
prompt = self.format_prompt(message, history)
inputs = self.tokenizer(prompt, return_tensors="pt", padding=True).to("cuda")
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
pad_token_id=self.tokenizer.pad_token_id, # Explicitly set the pad_token_id
)
output_ids = self.model.generate(**inputs, **generate_kwargs)
output = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
return output[len(prompt):].strip()
def classify_mood(input_string):
input_string = input_string.lower()
mood_words = {"happy", "sad", "instrumental", "party"}
for word in mood_words:
if word in input_string:
return word, True
return None, False
def speech_to_text(speech):
asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
text = asr(speech)["text"]
return text
def text_to_speech(text):
tts = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech")
speech = tts(text)["audio"]
return speech
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Start the Mistral chatbot application.")
parser.add_argument("--model_name", type=str, default="mistralai/Mistral-7B-Instruct-v0.2", help="The name of the model to use.")
args = parser.parse_args()
model_name = args.model_name
# Instantiate the chatbot with necessary parameters
mistral_chatbot = llmChatbot(model_name=model_name)
history = []
print("How are you doing today?")
def chatbot_response(audio_input):
text_input = speech_to_text(audio_input)
result = mistral_chatbot.generate(text_input, history)
mood, is_classified = classify_mood(result)
if is_classified:
response_text = mood.capitalize()
else:
response_text = result
audio_output = text_to_speech(response_text)
history.append((text_input, response_text))
return audio_output, response_text
gr.Interface(
fn=chatbot_response,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs=[gr.Audio(type="numpy"), "text"],
live=True
).launch()
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