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Update pages/AI_Chatbot.py
Browse files- pages/AI_Chatbot.py +25 -23
pages/AI_Chatbot.py
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@@ -2,32 +2,34 @@ import openai
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import os
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import streamlit as st
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openai.api_key = os.getenv("OPENAI_API_KEY")
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st.title("AI Chatbot")
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.markdown(prompt)
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with st.chat_message("assistant"):
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stream = openai.ChatCompletion.create(
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model=st.session_state["openai_model"],
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messages=[
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{"role": m["role"], "content": m["content"]}
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for m in st.session_state.messages
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],
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stream=True,
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)
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response = st.write_stream(stream)
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st.session_state.messages.append({"role": "assistant", "content": response})
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import os
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import streamlit as st
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openai.api_key = os.getenv("OPENAI_API_KEY")
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from langchain.document_loaders import PyPDFLoader
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st.title("AI Chatbot")
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uploaded_file = st.file_uploader("Choose a file")
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def extract(uploaded_file):
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res = []
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loader = PyPDFLoader(link)
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pages = loader.load()
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for i in pages:
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res.append(i.page_content.replace('\n',''))
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a = " ".join(res)
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return a
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context = extract(uploaded_file)
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def lang(ques):
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docs = Document(page_content=context)
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index2 = VectorstoreIndexCreator().from_documents([docs])
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answer = index2.query(llm = model, question = ques)
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index2.vectorstore.delete_collection()
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return answer
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def qna(uploaded_file,ques):
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session_state['answer']= lang(jury_url)
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st.title("Jury Records")
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ques= st.text_area(label= "Please enter the Question that you wanna ask.",
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placeholder="Question")
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st.text_area("result", value=session_state['answer'])
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st.button("Get answer dictionary", on_click=qna, args=[ques])
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