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Update app.py
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app.py
CHANGED
@@ -68,7 +68,7 @@ class Journal:
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def __repr__(self):
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return f"Journal(name='{self.name}', bytes='{self.bytes}')"
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llm = ChatOpenAI(temperature=0, model="gpt-
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textex_chain = create_extraction_chain(textex_schema, llm)
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tablex_chain = create_extraction_chain(tablex_schema, llm)
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@@ -104,7 +104,7 @@ if uploaded_files:
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if on_h:
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chunk_size_h = st.selectbox(
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'Tokens amounts per process :',
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(
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)
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parseButtonH = st.button("Get Result", key='table_H')
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@@ -116,7 +116,7 @@ if uploaded_files:
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if on_v:
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chunk_size_v = st.selectbox(
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'Tokens amounts per process :',
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(
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)
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parseButtonV = st.button("Get Result", key='table_V')
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with col3:
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@@ -127,7 +127,7 @@ if uploaded_files:
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if on_t:
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chunk_size_t = st.selectbox(
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'Tokens amounts per process :',
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(
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)
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parseButtonT = st.button("Get Result", key="no_Table")
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@@ -198,7 +198,7 @@ if uploaded_files:
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embeddings = OpenAIEmbeddings()
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db = Chroma.from_documents(docs, embeddings)
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llm_table = ChatOpenAI(model_name="gpt-
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qa_chain = RetrievalQA.from_chain_type(llm_table, retriever=db.as_retriever())
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# List of questions
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@@ -650,7 +650,7 @@ if uploaded_files:
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embeddings = OpenAIEmbeddings()
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db = Chroma.from_documents(docs, embeddings)
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llm_table = ChatOpenAI(model_name="gpt-
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qa_chain = RetrievalQA.from_chain_type(llm_table, retriever=db.as_retriever())
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# List of questions
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def __repr__(self):
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return f"Journal(name='{self.name}', bytes='{self.bytes}')"
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llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106")
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textex_chain = create_extraction_chain(textex_schema, llm)
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tablex_chain = create_extraction_chain(tablex_schema, llm)
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if on_h:
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chunk_size_h = st.selectbox(
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'Tokens amounts per process :',
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(15000, 12000, 10000, 8000, 5000), key='table_h'
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)
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parseButtonH = st.button("Get Result", key='table_H')
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if on_v:
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chunk_size_v = st.selectbox(
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'Tokens amounts per process :',
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(15000, 12000, 10000, 8000, 5000), key='table_v'
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)
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parseButtonV = st.button("Get Result", key='table_V')
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with col3:
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if on_t:
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chunk_size_t = st.selectbox(
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'Tokens amounts per process :',
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(15000, 12000, 10000, 8000, 5000), key='no_table'
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)
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parseButtonT = st.button("Get Result", key="no_Table")
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embeddings = OpenAIEmbeddings()
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db = Chroma.from_documents(docs, embeddings)
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llm_table = ChatOpenAI(model_name="gpt-3.5-turbo-1106", temperature=0)
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qa_chain = RetrievalQA.from_chain_type(llm_table, retriever=db.as_retriever())
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# List of questions
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embeddings = OpenAIEmbeddings()
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db = Chroma.from_documents(docs, embeddings)
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llm_table = ChatOpenAI(model_name="gpt-3.5-turbo-1106", temperature=0)
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qa_chain = RetrievalQA.from_chain_type(llm_table, retriever=db.as_retriever())
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# List of questions
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