KKowenn commited on
Commit
1fe0c1f
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verified ·
1 Parent(s): c84b881

Update app.py

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Files changed (1) hide show
  1. app.py +15 -15
app.py CHANGED
@@ -341,32 +341,32 @@ else:
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  if st.button("Analyze"):
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  # Ensure full extracted text is used for analysis
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  text_for_analysis = st.session_state["pdf_text"].strip() if st.session_state["pdf_text"] else example_text.strip()
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-
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  if text_for_analysis:
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  with st.spinner("Analyzing text..."):
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  # Extract structured financial data using regex (Now using full text)
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  extracted_data = {
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  key: (match.group(1) if match else "N/A")
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  for key, pattern in patterns.items()
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- if (match := re.search(pattern, text_for_analysis, re.IGNORECASE))
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- }
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- # Use spaCy to extract additional financial terms (Now using full text)
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- doc = nlp(text_for_analysis)
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- financial_entities = [(ent.text, ent.label_) for ent in doc.ents if ent.label_ in ["MONEY", "PERCENT", "ORG", "DATE"]]
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- # Store extracted data in a structured dictionary
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- structured_data = {**extracted_data, "Named Entities Extracted": financial_entities}
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- # Display results
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- st.write("Entities Found:")
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- st.write(pd.DataFrame(financial_entities, columns=["Entity", "Label"]))
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- st.write("Structured Data Extracted:")
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- st.write(pd.DataFrame([structured_data]))
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- else:
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- st.error("Please provide some text for analysis.")
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  # Step 4: Summarization
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  st.subheader("Summarization")
 
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  if st.button("Analyze"):
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  # Ensure full extracted text is used for analysis
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  text_for_analysis = st.session_state["pdf_text"].strip() if st.session_state["pdf_text"] else example_text.strip()
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+
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  if text_for_analysis:
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  with st.spinner("Analyzing text..."):
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  # Extract structured financial data using regex (Now using full text)
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  extracted_data = {
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  key: (match.group(1) if match else "N/A")
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  for key, pattern in patterns.items()
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+ if (match := re.search(pattern, text_for_analysis, re.IGNORECASE))
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+ }
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+ # Correct indentation
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+ doc = nlp(text_for_analysis)
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+ financial_entities = [(ent.text, ent.label_) for ent in doc.ents if ent.label_ in ["MONEY", "PERCENT", "ORG", "DATE"]]
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+ # Store extracted data in a structured dictionary
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+ structured_data = {**extracted_data, "Named Entities Extracted": financial_entities}
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+ # Display results
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+ st.write("Entities Found:")
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+ st.write(pd.DataFrame(financial_entities, columns=["Entity", "Label"]))
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+ st.write("Structured Data Extracted:")
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+ st.write(pd.DataFrame([structured_data]))
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+ else:
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+ st.error("Please provide some text for analysis.")
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  # Step 4: Summarization
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  st.subheader("Summarization")