first commit

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import pinecone
+from makechain import get_chain
+from langchain.vectorstores.pinecone import Pinecone
+from env import PINECONE_INDEX_NAME, PINECONE_ENVIRONMENT, PINECONE_API_KEY, OPENAI_API_KEY
+from langchain.embeddings.openai import OpenAIEmbeddings
+
+
+st.title("Ask the Black@Stanford Exhibit")
+st.sidebar.header("You can ask questions of interviews with Black Stanford students and faculty from the University "
+                  "Archives")
+st.sidebar.info(
+    '''This is a web application that allows you to interact with 
+       the Stanford Archives.
+       Enter a **Question** in the **text box** and **press enter** to receive 
+       a **response** from our ChatBot.
+       '''
+)
+
+# create Vectorstore
+pinecone.init(
+    api_key=st.secrets["PINECONE_API_KEY"],  # find at app.pinecone.io
+    environment=st.secrets["PINECONE_ENVIRONMENT"]  # next to api key in console
+)
+index = pinecone.Index(index_name=st.secrets["PINECONE_INDEX_NAME"])
+embed = OpenAIEmbeddings(openai_api_key=st.secrets["OPENAI_API_KEY"])
+text_field = "text"
+vectorStore = Pinecone(
+    index, embed.embed_query, text_field
+)
+
+# create chain
+qa_chain = get_chain(vectorStore)
+
+def main():
+    global query
+
+    user_query= st.text_input("Enter your question here")
+    if user_query != ":q" or user_query != "":
+        # Pass the query to the ChatGPT function
+        query = user_query.strip().replace('\n', ' ')
+        response = qa_chain(
+            {
+                'question': query,
+            }
+        )
+        st.write(f"{response['answer']}")
+        st.write("Sources: ")
+        st.write(f"{response['sources']}")
+
+try:
+    main()
+except Exception as e:
+    st.write("An error occurred while running the application: ", e)
+
+
+
+
+
+
+
+
+
+

requirements.txt

@@ -0,0 +1,5 @@
+streamlit~=1.22.0
+pinecone-client
+langchain~=0.0.177
+openai~=0.27.7
+tqdm~=4.65.0