TAD.py

from ollama import Client
import ollama
import chromadb
import speech_recognition as sr
import requests 
import pyttsx3


client = chromadb.Client()

message_history = [

    {
        'id' : 1,
        'prompt' : 'What is your name?',
        'response' : 'My name is TADBot,  a bot to help with short term remedial help for mental purposes. '

    },

    {
        'id' : 2,
        'prompt' : 'Bye',
        'response' : 'Good to see you get better. Hopefully you reach out to me if you have any problems.'

    },

    {
        'id' : 3,
        'prompt' : 'What is the essence of Life?',
        'response' : 'The essence of life is to create what you want of yourself.'

    }
]
convo = []

llm = Client(host='http://localhost:11434')


def create_vector_db(conversations):

    vector_db_name = 'conversations'

    try: 
        client.delete_collection(vector_db_name)
    except ValueError as e:
        pass

    vector_db = client.create_collection(name=vector_db_name)
    for c in conversations:

        serialized_convo = 'prompt: ' + c["prompt"] + ' response: ' + c["response"]

        response = ollama.embeddings(model = "nomic-embed-text",prompt = serialized_convo)
        embedding = response["embedding"]

        vector_db.add(ids = [str(c['id'])], embeddings = [embedding], documents = [serialized_convo])


def stream_response(prompt):
    
    convo.append({'role': "user", 'content': prompt})
    output = llm.chat(model = "TADBot", messages = convo)
    response = output['message']['content']

    print("TADBot: ") 
    print(response)
    engine = pyttsx3.init('espeak')
    engine.say(response)
    engine.runAndWait()

    convo.append({'role': "assistant", 'content': response})

def retrieve_embeddings(prompt):
    response = ollama.embeddings(model = "nomic-embed-text", prompt = prompt)
    propmt_embedding = response['embedding']

    vector_db = client.get_collection(name = 'conversations')
    results = vector_db.query(query_embeddings=[propmt_embedding], n_results = 1)

    best_embedding = results['documents'][0][0]
    return best_embedding

create_vector_db(message_history)


while True: 
    
    r = sr.Recognizer()
    m = sr.Microphone()
    
    try:


        print("Say something!")
        with m as source: 
            audio = r.listen(source)


        try:
            # for testing purposes, we're just using the default API key
            # to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
            # instead of `r.recognize_google(audio)`
            prompt = r.recognize_google(audio)
            print("Tadbot thinks you said: " + prompt)
        except sr.UnknownValueError:
            print("Tadbot could not understand audio")
        except sr.RequestError as e:
            print("Could not request results from Google Speech Recognition service; {0}".format(e))


        print("Please wait...")
        with m as source: 
            r.adjust_for_ambient_noise(source)

        if  prompt == "bye" or prompt == "Bye": 
            print("TADBot: Hopefully I was able to help you out today. Have a Nice Day!")
            break
        """
        context = retrieve_embeddings(prompt)

        prompt = prompt + "CONTEXT FROM EMBEDDING: " + context
        """
        stream_response(prompt)
    except KeyboardInterrupt:
        pass