Spaces:
Runtime error
Runtime error
added open ai key
Browse files- app.py +0 -59
- mental_health_raqa.py +1 -1
app.py
CHANGED
|
@@ -1,65 +1,6 @@
|
|
| 1 |
import streamlit as st
|
| 2 |
from mental_health_raqa import mh_assistant
|
| 3 |
|
| 4 |
-
# #---------------------------------#
|
| 5 |
-
# import pandas as pd
|
| 6 |
-
# import os
|
| 7 |
-
# from langchain.document_loaders.csv_loader import CSVLoader
|
| 8 |
-
# from langchain.embeddings.openai import OpenAIEmbeddings
|
| 9 |
-
# from langchain.embeddings import CacheBackedEmbeddings
|
| 10 |
-
# from langchain_community.vectorstores import FAISS
|
| 11 |
-
# from langchain.storage import LocalFileStore
|
| 12 |
-
# from langchain_openai import ChatOpenAI
|
| 13 |
-
# from langchain.chains import RetrievalQA
|
| 14 |
-
# from langchain.callbacks import StdOutCallbackHandler
|
| 15 |
-
|
| 16 |
-
# def create_index():
|
| 17 |
-
# # load the data
|
| 18 |
-
# dir = os.path.dirname(__file__)
|
| 19 |
-
# df_path = dir + '/data/Mental_Health_FAQ.csv'
|
| 20 |
-
# loader = CSVLoader(file_path = df_path)
|
| 21 |
-
# data = loader.load()
|
| 22 |
-
|
| 23 |
-
# # create the embeddings model
|
| 24 |
-
# embeddings_model = OpenAIEmbeddings()
|
| 25 |
-
|
| 26 |
-
# # create the cache backed embeddings in vector store
|
| 27 |
-
# store = LocalFileStore("./cache")
|
| 28 |
-
# cached_embeder = CacheBackedEmbeddings.from_bytes_store(
|
| 29 |
-
# embeddings_model, store, namespace=embeddings_model.model
|
| 30 |
-
# )
|
| 31 |
-
# vector_store = FAISS.from_documents(data, embeddings_model)
|
| 32 |
-
|
| 33 |
-
# return vector_store.as_retriever()
|
| 34 |
-
|
| 35 |
-
# def setup(openai_key):
|
| 36 |
-
# # Set the API key for OpenAI
|
| 37 |
-
# os.environ["OPENAI_API_KEY"] = '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'
|
| 38 |
-
# retriver = create_index()
|
| 39 |
-
# llm = ChatOpenAI(model="gpt-4")
|
| 40 |
-
# return retriver, llm
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
# def mh_assistant(openai_key,query):
|
| 44 |
-
|
| 45 |
-
# # Setup
|
| 46 |
-
# retriever,llm = setup(openai_key)
|
| 47 |
-
# # Create the QA chain
|
| 48 |
-
# handler = StdOutCallbackHandler()
|
| 49 |
-
|
| 50 |
-
# qa_with_sources_chain = RetrievalQA.from_chain_type(
|
| 51 |
-
# llm=llm,
|
| 52 |
-
# retriever=retriever,
|
| 53 |
-
# callbacks=[handler],
|
| 54 |
-
# return_source_documents=True
|
| 55 |
-
# )
|
| 56 |
-
|
| 57 |
-
# # Ask a question
|
| 58 |
-
# res = qa_with_sources_chain({"query":query})
|
| 59 |
-
# return (res['result'])
|
| 60 |
-
# # (mh_assistant("sadfs",'what is mental health?'))
|
| 61 |
-
|
| 62 |
-
#---------------------------------#
|
| 63 |
st.title('Mental Health Assistant :broken_heart:')
|
| 64 |
|
| 65 |
# Create a text input box for the OpenAI key
|
|
|
|
| 1 |
import streamlit as st
|
| 2 |
from mental_health_raqa import mh_assistant
|
| 3 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
st.title('Mental Health Assistant :broken_heart:')
|
| 5 |
|
| 6 |
# Create a text input box for the OpenAI key
|
mental_health_raqa.py
CHANGED
|
@@ -30,7 +30,7 @@ def create_index():
|
|
| 30 |
|
| 31 |
def setup(openai_key):
|
| 32 |
# Set the API key for OpenAI
|
| 33 |
-
os.environ["OPENAI_API_KEY"] =
|
| 34 |
retriver = create_index()
|
| 35 |
llm = ChatOpenAI(model="gpt-4")
|
| 36 |
return retriver, llm
|
|
|
|
| 30 |
|
| 31 |
def setup(openai_key):
|
| 32 |
# Set the API key for OpenAI
|
| 33 |
+
os.environ["OPENAI_API_KEY"] = openai_key
|
| 34 |
retriver = create_index()
|
| 35 |
llm = ChatOpenAI(model="gpt-4")
|
| 36 |
return retriver, llm
|