LLM.py

from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.schema.output_parser import StrOutputParser
from dotenv import load_dotenv
from huggingface_hub import login
import os

# Load environment variables
load_dotenv()
login(token=os.environ["HUGGING_FACE_API_KEY"])
os.environ['CURL_CA_BUNDLE'] = ''  # Optional workaround for certificate issues

class Bot():
    def __init__(self):
        self.groq_models = ['gemma-7b-it', 'llama3-70b-8192',
                            'llama3-8b-8192', 'mixtral-8x22b']  # Updated here
        self.hf_models = ["01-ai/Yi-1.5-34B-Chat", "google/gemma-1.1-2b-it",
                          "google/gemma-1.1-7b-it"]
        self.google_models = ["gemini-1.0-pro", "gemini-1.5-flash",
                              "gemini-1.5-pro"]

        # Main model pool
        self.models = self.google_models + self.hf_models + self.groq_models

    def call_groq(self, model, temp=0.7, given_prompt="Hi"):
        try:
            llm = ChatGroq(
                model=model,
                temperature=temp,
                groq_api_key=os.environ["GROQ_API_KEY"]
            )
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are a helpful assistant."),
                ("human", "{text}")
            ])
            chain = prompt | llm | StrOutputParser()
            return chain.invoke({"text": given_prompt})

        except Exception as e:
            return f"Error (Groq): {str(e)}"

    def call_hf(self, model, temp=0.7, given_prompt="Hi"):
        try:
            llm = HuggingFaceEndpoint(
                repo_id=model,
                temperature=temp
            )
            chat = ChatHuggingFace(llm=llm, verbose=True)
            prompt = PromptTemplate(
                template="""
                    You are a helpful assistant

                    User: {query}

                    Answer:
                """,
                input_variables=["query"]
            )
            chain = prompt | chat | StrOutputParser()
            return chain.invoke({"query": given_prompt})

        except Exception as e:
            return f"Error (HF): {str(e)}"

    def call_google(self, model, temp=0.7, given_prompt="Hi"):
        try:
            llm = ChatGoogleGenerativeAI(
                model=model,
                temperature=temp,
                google_api_key=os.environ["GOOGLE_API_KEY"]
            )
            prompt = ChatPromptTemplate.from_messages([
                ("human", "{text}")
            ])
            chain = prompt | llm | StrOutputParser()
            return chain.invoke({"text": given_prompt})

        except Exception as e:
            return f"Error (Google): {str(e)}"

    def response(self, model, prompt="Hi", temperature=0.7):
        if model in self.groq_models:
            return self.call_groq(model=model, temp=temperature, given_prompt=prompt)
        elif model in self.hf_models:
            return self.call_hf(model=model, temp=temperature, given_prompt=prompt)
        elif model in self.google_models:
            return self.call_google(model=model, temp=temperature, given_prompt=prompt)
        else:
            return "Sorry! App not working properly – unknown model"