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# from langchain_community.llms import HuggingFaceEndpoint | |
# from langchain.prompts import PromptTemplate | |
# from langchain.schema import AIMessage, HumanMessage | |
# from langchain.chains import LLMChain | |
import gradio as gr | |
import os | |
from crew import CryptoCrew | |
from dotenv import load_dotenv | |
load_dotenv() | |
def predict(message): | |
# company = input( | |
# dedent(""" | |
# Which cryptocurrency are you looking to delve into? | |
# """)) | |
# history_langchain_format = [] | |
# for human, ai in history: | |
# history_langchain_format.append(HumanMessage(content=human)) | |
# history_langchain_format.append(AIMessage(content=ai)) | |
# history_langchain_format.append(HumanMessage(content=message)) | |
# gpt_response = llm(history_langchain_format) | |
# response = llm_chain.invoke(message)['text'] | |
crypto_crew = CryptoCrew(company) | |
response = "## Here is the Report\n\n" + crypto_crew.run() | |
return response | |
gr.ChatInterface(predict).launch() | |
# if __name__ == "__main__": | |
# print("## Welcome to Crypto Analysis Crew") | |
# print('-------------------------------') | |
# company = input( | |
# dedent(""" | |
# Which cryptocurrency are you looking to delve into? | |
# """)) | |
# crypto_crew = CryptoCrew(company) | |
# result = crypto_crew.run() | |
# print("\n\n########################") | |
# print("## Here is the Report") | |
# print("########################\n") | |
# print(result) | |