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import os
from dotenv import load_dotenv
import streamlit as st
# from genai.credentials import Credentials
# from genai.schemas import GenerateParams
# from genai.model import Model
from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes
from ibm_watson_machine_learning.foundation_models import Model
from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
from langchain_ibm import WatsonxLLM
#load_dotenv()
# api_key = os.getenv("GENAI_KEY", None)
# api_endpoint = os.getenv("GENAI_API", None)
# creds = Credentials(api_key,api_endpoint)
# params = GenerateParams(
# decoding_method="sample",
# max_new_tokens=200,
# min_new_tokens=1,
# stream=False,
# temperature=0.7,
# top_k=50,
# top_p=1,
# stop_sequences= ["Human:","AI:"],
# )
api_key = os.getenv("API_KEY")
project_id = os.getenv("PROJECT_ID")
creds = {
"url" : "https://us-south.ml.cloud.ibm.com",
"apikey" : api_key
}
params = {
GenParams.DECODING_METHOD:"sample",
GenParams.MAX_NEW_TOKENS:200,
GenParams.MIN_NEW_TOKENS:1,
GenParams.TEMPERATURE:0.7,
GenParams.TOP_K:50,
GenParams.TOP_P:1,
GenParams.STOP_SEQUENCES: ["Human:","AI:"]
}
with st.sidebar:
st.title("WATSONX CHAT")
st.write("WATSONX.AI")
st.write("RAHMAN")
st.title("CHAT WITH WATSONX")
with st.chat_message("system"):
st.write("Hello 👋, lets chat with watsonx")
if "messages" not in st.session_state:
st.session_state.messages = []
llm = Model(ModelTypes.LLAMA_2_70B_CHAT,creds,params,project_id)
# llm = Model(model="meta-llama/llama-2-7b-chat",credentials=creds, params=params)
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Say something"):
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
prompttemplate = f"""
[INST]<<SYS>>Respond in English<<SYS>>
{prompt}
[/INST]
"""
response_text = llm.generate_text(prompttemplate)
answer = response_text
# for response in response_text[0].generated_text
# answer += response[0].generated_text
st.session_state.messages.append({"role": "agent", "content": answer})
with st.chat_message("agent"):
st.markdown(answer)
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