Update lessonsummarize.py

lessonsummarize.py

@@ -1,75 +1,75 @@
-import streamlit as st
-import time
-import langchain
-import openai
-from langchain.llms import OpenAI
-from langchain.vectorstores import Chroma
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.chains import RetrievalQA
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import PromptTemplate
-from langchain.embeddings.openai import OpenAIEmbeddings
-
-
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-
-
-openai_key = "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"
-
-
-def summarize():
-    # st.markdown('<h1 style="font-family:Lora;color:darkred;text-align:center;">Summarize Your Lesson</h1>',unsafe_allow_html=True)
-    # st.markdown('<i><h3 style="font-family:Arial;color:darkred;text-align:center;font-size:20px;padding-left:50px">Your AI Assistant To Summarize Lessons To Help You Cover Bullet Points!</h3><i>',unsafe_allow_html=True)
-
-    uploaded_file = st.file_uploader("Upload PDF File Of Your Lesson")
-
-    if uploaded_file:
-        with st.spinner("Summarizing lesson into bullet points..."):
-            with open(uploaded_file.name, mode='wb') as w:
-                w.write(uploaded_file.getvalue())
-            loader = PyPDFLoader(uploaded_file.name)
-            pages = loader.load()
-            
-            # Split
-            from langchain.text_splitter import RecursiveCharacterTextSplitter
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size = 1500,
-                chunk_overlap = 150
-            )
-
-            splits = text_splitter.split_documents(pages)
-
-            embedding = OpenAIEmbeddings(api_key="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")
-
-            persist_directory = 'docs/chroma/'
-
-            vectordb = Chroma.from_documents(
-                documents=splits,
-                embedding=embedding,
-                persist_directory=persist_directory
-            )
-
-            llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, api_key = openai_key)
-                    
-            template = """Use the following pieces of context and summarize the whole lesson for the teacher in bullet point to help teachers understand lesson. If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer as concise as possible.  
-            {context}
-            Question: {question}
-            Helpful Answer:"""
-
-            QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template)
-
-            # Run chain
-            qa_chain = RetrievalQA.from_chain_type(
-                llm,
-                retriever=vectordb.as_retriever(),
-                chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
-            )
-
-            result = qa_chain({"query": "Summarize this lesson for me. I am a teacher, I need to better understand this lesson. put it in bullet points"})
-
-            st.success(result['result'])
-
-            vectordb.delete_collection()
-
+import streamlit as st
+import time
+import langchain
+import openai
+from langchain.llms import OpenAI
+from langchain.vectorstores import Chroma
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.embeddings.openai import OpenAIEmbeddings
+
+
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+import os
+
+openai_key = os.environ["key"]
+
+
+def summarize():
+    # st.markdown('<h1 style="font-family:Lora;color:darkred;text-align:center;">Summarize Your Lesson</h1>',unsafe_allow_html=True)
+    # st.markdown('<i><h3 style="font-family:Arial;color:darkred;text-align:center;font-size:20px;padding-left:50px">Your AI Assistant To Summarize Lessons To Help You Cover Bullet Points!</h3><i>',unsafe_allow_html=True)
+
+    uploaded_file = st.file_uploader("Upload PDF File Of Your Lesson")
+
+    if uploaded_file:
+        with st.spinner("Summarizing lesson into bullet points..."):
+            with open(uploaded_file.name, mode='wb') as w:
+                w.write(uploaded_file.getvalue())
+            loader = PyPDFLoader(uploaded_file.name)
+            pages = loader.load()
+            
+            # Split
+            from langchain.text_splitter import RecursiveCharacterTextSplitter
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size = 1500,
+                chunk_overlap = 150
+            )
+
+            splits = text_splitter.split_documents(pages)
+
+            embedding = OpenAIEmbeddings(api_key="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")
+
+            persist_directory = 'docs/chroma/'
+
+            vectordb = Chroma.from_documents(
+                documents=splits,
+                embedding=embedding,
+                persist_directory=persist_directory
+            )
+
+            llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, api_key = openai_key)
+                    
+            template = """Use the following pieces of context and summarize the whole lesson for the teacher in bullet point to help teachers understand lesson. If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer as concise as possible.  
+            {context}
+            Question: {question}
+            Helpful Answer:"""
+
+            QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template)
+
+            # Run chain
+            qa_chain = RetrievalQA.from_chain_type(
+                llm,
+                retriever=vectordb.as_retriever(),
+                chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
+            )
+
+            result = qa_chain({"query": "Summarize this lesson for me. I am a teacher, I need to better understand this lesson. put it in bullet points"})
+
+            st.success(result['result'])
+
+            vectordb.delete_collection()
+