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from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace
from langchain_huggingface import HuggingFaceEndpoint
from langchain_google_genai import ChatGoogleGenerativeAI
from dotenv import load_dotenv
from huggingface_hub import login
import os
# Load environment variables and authenticate
load_dotenv()
login(token=os.environ.get("HUGGING_FACE_API_KEY", ""))
os.environ['CURL_CA_BUNDLE'] = ''
class Bot:
def __init__(self):
# Updated model lists: Remove gated or unsupported IDs
self.groq_models = [
'gemma-7b-it',
'llama3-70b-8192',
'llama3-8b-8192',
'mixtral-8x22b'
]
self.hf_models = [
"01-ai/Yi-1.5-34B-Chat",
"google/gemma-1.1-2b-it"
]
# Use supported Google GenAI model names
self.google_models = [
"gemini-pro",
"gemini-pro-vision"
]
# Master list for sampling (only include accessible models)
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)
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"⚠️ [Groq:{model}] {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)
template = """
You are a helpful assistant.
User: {query}
Answer:
"""
prompt = PromptTemplate(template=template, input_variables=["query"])
chain = prompt | chat | StrOutputParser()
return chain.invoke({"query": given_prompt})
except Exception as e:
return f"⚠️ [HF:{model}] {str(e)}"
def call_google(self, model, temp=0.7, given_prompt="Hi"):
try:
gm = ChatGoogleGenerativeAI(model=model, temperature=temp)
prompt = ChatPromptTemplate.from_messages([("system", "You are a helpful assistant."), ("human", "{text}")])
chain = prompt | gm | StrOutputParser()
return chain.invoke({"text": given_prompt})
except Exception as e:
return f"⚠️ [Google:{model}] {str(e)}"
def response(self, model, prompt="Hi", temperature=0.7):
# Route to the correct provider and catch errors
try:
if model in self.groq_models:
return self.call_groq(model, temp=temperature, given_prompt=prompt)
if model in self.hf_models:
return self.call_hf(model, temp=temperature, given_prompt=prompt)
if model in self.google_models:
return self.call_google(model, temp=temperature, given_prompt=prompt)
return f"❌ Unsupported model: {model}"
except Exception as e:
return f"⚠️ Skipping `{model}` due to error: {str(e)}"
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