mikeion commited on
Commit
d510719
·
1 Parent(s): 71e751b

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +35 -34
app.py CHANGED
@@ -152,8 +152,8 @@ class Chatbot():
152
  print(sources)
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  return results.head(n)
154
 
155
- def create_prompt(self, df, user_input):
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- result = self.search_embeddings(df, user_input, n=3)
157
  print(result)
158
  prompt = """You are a large language model whose expertise is reading and and providing answers about research papers.
159
  You are given a query and a series of text embeddings from a paper in order of their cosine similarity to the query.
@@ -216,41 +216,42 @@ def show_pdf(file_content):
216
 
217
  def main():
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  st.title("Research Paper Guru")
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- st.subheader("Ask a question about a research paper and get an answer with sources!")
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  st.subheader("Upload PDF or Enter URL")
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-
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- pdf_option = st.selectbox("Choose an option:", ["Upload PDF", "Enter URL"])
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- chatbot = Chatbot()
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-
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- if pdf_option == "Upload PDF":
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- uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
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- if uploaded_file is not None:
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- file_content = uploaded_file.read()
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- process_pdf(file_content)
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- st.success("PDF uploaded and processed successfully!")
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- show_pdf(file_content)
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- elif pdf_option == "Enter URL":
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- url = st.text_input("Enter the URL of the PDF:")
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- if url:
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- if st.button("Download and process PDF"):
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- try:
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- r = requests.get(str(url))
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- content = r.content
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- download_pdf(url)
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- st.success("PDF downloaded and processed successfully!")
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- show_pdf(content)
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- except Exception as e:
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- st.error(f"An error occurred while processing the PDF: {e}")
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- query = st.text_input("Enter your query:")
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- if query:
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- if st.button("Get answer"):
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- response = chatbot.reply(query)
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- st.write(response['answer'])
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- st.write("Sources:")
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- for source in response['sources']:
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- st.write(source)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  if __name__ == "__main__":
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  main()
 
152
  print(sources)
153
  return results.head(n)
154
 
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+ def create_prompt(self, embeddings, df, user_input):
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+ result = self.search_embeddings(df, embeddings, user_input, n=3)
157
  print(result)
158
  prompt = """You are a large language model whose expertise is reading and and providing answers about research papers.
159
  You are given a query and a series of text embeddings from a paper in order of their cosine similarity to the query.
 
216
 
217
  def main():
218
  st.title("Research Paper Guru")
 
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  st.subheader("Upload PDF or Enter URL")
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+ col1, col2 = st.columns(2)
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+ with col1:
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+ pdf_option = st.selectbox("Choose an option:", ["Upload PDF", "Enter URL"])
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+ chatbot = Chatbot()
 
 
 
 
 
 
 
224
 
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+ if pdf_option == "Upload PDF":
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+ uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
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+ if uploaded_file is not None:
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+ file_content = uploaded_file.read()
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+ process_pdf(file_content)
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+ st.success("PDF uploaded and processed successfully!")
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+ show_pdf(file_content)
 
 
 
 
 
232
 
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+ elif pdf_option == "Enter URL":
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+ url = st.text_input("Enter the URL of the PDF:")
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+ if url:
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+ if st.button("Download and process PDF"):
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+ try:
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+ r = requests.get(str(url))
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+ content = r.content
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+ download_pdf(url)
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+ st.success("PDF downloaded and processed successfully!")
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+ show_pdf(content)
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+ except Exception as e:
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+ st.error(f"An error occurred while processing the PDF: {e}")
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+ with col2:
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+ st.subheader("Ask a question about a research paper and get an answer with sources!")
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+ query = st.text_input("Enter your query:")
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+ if query:
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+ if st.button("Get answer"):
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+ response = chatbot.reply(query)
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+ st.write(response['answer'])
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+ st.write("Sources:")
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+ for source in response['sources']:
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+ st.write(source)
255
 
256
  if __name__ == "__main__":
257
  main()