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modify 2 page to 1 main page
Browse files
app.py
CHANGED
@@ -49,93 +49,74 @@ def load_model():
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print(f"Error loading model: {e}")
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return None
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#
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def
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global qa_chain
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try:
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pipe = load_model()
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except Exception as e:
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print(f"Error loading model: {e}")
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return
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llm = HuggingFacePipeline(pipeline=pipe)
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qa_chain = load_qa_chain(llm, chain_type="map_rerank")
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# st.session_state.paper_name = paper.name[:-4]
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st.session_state.page = "chat"
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st.success("PDF successfully processed! You can now ask questions.")
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# ๋ฉ์ธ ํ์ด์ง UI
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def main_page():
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st.title("Welcome to GemmaPaperQA")
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st.write(f"Total characters extracted: {len(text)}")
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global knowledge_base
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if st.button("Proceed with this file"):
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chunks = split_text(text)
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knowledge_base = create_knowledge_base(chunks)
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if knowledge_base is None:
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st.error("Failed to create knowledge base.")
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return
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setup_qa_chain()
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except Exception as e:
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st.error(f"Failed to process the PDF: {str(e)}")
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# ๋ชจ๋ธ ์๋ต ์ฒ๋ฆฌ
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def get_response_from_model(prompt):
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@@ -156,15 +137,7 @@ def get_response_from_model(prompt):
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except Exception as e:
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return f"Error: {str(e)}"
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# ํ์ด์ง ์ค์
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if "page" not in st.session_state:
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st.session_state.page = "main"
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if "paper_name" not in st.session_state:
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st.session_state.paper_name = ""
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#
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if
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elif st.session_state.page == "chat":
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chat_page()
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print(f"Error loading model: {e}")
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return None
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# ํ์ด์ง UI
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def main():
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st.title("Welcome to GemmaPaperQA")
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# PDF ์
๋ก๋ ์น์
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with st.expander("Upload Your Paper", expanded=True):
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paper = st.file_uploader("Upload Here!", type="pdf", label_visibility="hidden")
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if paper:
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st.write(f"Upload complete! File name: {paper.name}")
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# ํ์ผ ํฌ๊ธฐ ํ์ธ
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file_size = paper.size # ํ์ผ ํฌ๊ธฐ๋ฅผ ํ์ผ ํฌ์ธํฐ ์ด๋ ์์ด ํ์ธ
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if file_size > 10 * 1024 * 1024: # 10MB ์ ํ
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st.error("File is too large! Please upload a file smaller than 10MB.")
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return
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# PDF ํ
์คํธ ๋ฏธ๋ฆฌ๋ณด๊ธฐ
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with st.spinner('Processing PDF...'):
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try:
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paper.seek(0)
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contents = paper.read()
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pdf_file = io.BytesIO(contents)
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text = load_pdf(pdf_file)
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print("text:", text)
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if len(text.strip()) == 0:
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st.error("The PDF appears to have no extractable text. Please check the file and try again.")
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return
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st.text_area("Preview of extracted text", text[:1000], height=200)
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st.write(f"Total characters extracted: {len(text)}")
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if st.button("Create Knowledge Base"):
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global knowledge_base
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chunks = split_text(text)
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print("chunks:", chunks)
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knowledge_base = create_knowledge_base(chunks)
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print("knowledge_base:", knowledge_base)
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if knowledge_base is None:
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st.error("Failed to create knowledge base.")
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return
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# QA ์ฒด์ธ ์ค์
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global qa_chain
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try:
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pipe = load_model()
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except Exception as e:
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st.error(f"Error loading model: {e}")
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return
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llm = HuggingFacePipeline(pipeline=pipe)
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qa_chain = load_qa_chain(llm, chain_type="map_rerank")
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st.success("Knowledge base created! You can now ask questions.")
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except Exception as e:
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st.error(f"Failed to process the PDF: {str(e)}")
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# ์ง๋ฌธ-์๋ต ์น์
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if knowledge_base and qa_chain:
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with st.expander("Ask Questions", expanded=True):
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prompt = st.text_input("Chat here!")
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if prompt:
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response = get_response_from_model(prompt)
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if response:
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st.write(f"**Assistant**: {response}")
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# ๋ชจ๋ธ ์๋ต ์ฒ๋ฆฌ
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def get_response_from_model(prompt):
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except Exception as e:
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return f"Error: {str(e)}"
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# ์ฑ ์คํ
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if __name__ == "__main__":
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main()
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