Update app.py

app.py

@@ -0,0 +1,80 @@
+import streamlit as st
+from langchain.callbacks import StreamlitCallbackHandler
+import streamlit as st
+from langchain.llms import OpenAI
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.callbacks import StreamlitCallbackHandler
+import streamlit as st
+
+from scraping import prompty
+
+import os
+
+
+st.set_page_config(page_title="CalHacks2023", page_icon=":robot:")
+st.header("CalHacks2023")
+
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.document_loaders import TextLoader
+
+import os
+os.environ["OPENAI_API_KEY"] = "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"
+
+from langchain.chat_models import ChatOpenAI
+from langchain.chains.question_answering import load_qa_chain
+
+from langchain.chains import RetrievalQA
+
+if "generated" not in st.session_state:
+  st.session_state["generated"] = []
+
+if "past" not in st.session_state:
+  st.session_state["past"] = []
+
+if "messages" not in st.session_state:
+  st.session_state["messages"] = []
+
+# load the document and split it into chunks
+loader = TextLoader("/content/shermaissian_how_to.txt")
+documents = loader.load()
+
+# split it into chunks
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+# create the open-source embedding function
+embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# load it into Chroma
+db = Chroma.from_documents(docs, embedding_function)
+
+
+
+st_callback = StreamlitCallbackHandler(st.container())
+
+
+
+retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":10})
+
+qa = RetrievalQA.from_chain_type(
+    llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
+
+bool called = False
+
+if prompt := st.chat_input():
+    st.chat_message("user").write(prompt)
+    with st.chat_message("assistant"):
+        #st_callback = StreamlitCallbackHandler(st.container())
+        if not called:
+            result = qa({"query": "Image yourself in the perspective of a high school student trying to find extracurricular activities to do for college applications. The following shows a specific student answering questions about their passions and experiences:" + 
+                 prompt + 
+                 "Use the information provided to analyze the underlying passions this student has. Use their past experiences and interests to come up with a list of a few potential extracurricular activities that they can engage in to develop their college application. This has to fit both their interests and allow them to gain skills that will enable them to build a successful college application portfolio. Provide reasoning as to why you are recommending these activities and as part of the description as open-ended questions that challenge them to think more about these ideas. Discover the root cause of what drives them to change the world for a better place. Based on that, craft extracurriculars around a personalized, nuanced, and unique story that can make these applicants truly showcase how they’re creating an impact in novel, meaningful​​, and creative ways."})
+            retriever.get_relevant_documents(query)
+            st.write(result['result'])
+            called = True
+        else:
+            result = qa({"query" : prompt})
+            retriever.get_relevant_documents(query)
+            st.write(result['result'])