Update app.py

app.py

@@ -28,15 +28,11 @@ model = AutoModelForTokenClassification.from_pretrained("AhmedTaha012/finance-ne
 # torch.compile(model)
 nlpPipe = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
 
-
-
 if "disabled" not in st.session_state:
     st.session_state["disabled"] = False
-
 def disable():
     st.session_state["disabled"] = True
 
-
 def getSpeakers(data):
     if "Speakers" in data:
         return "\n".join([x for x in data.split("Speakers")[-1].split("\n") if "--" in x])
@@ -86,7 +82,6 @@ def clean_and_preprocess(text):
         l.append(cleaned_text)
     return "\n".join(l)
 def replace_abbreviations(text):
-    
     replacements = {
         'Q1': 'first quarter',
         'Q2': 'second quarter',
@@ -142,39 +137,39 @@ def replace_abbreviations(text):
         'EBITDAR margin': 'earnings before interest, taxes, depreciation, amortization, and rent margin',
         'ROIC': 'return on invested capital',
         'EPS': 'earnings per share',
-    'P/E': 'price-to-earnings',
-    'EBITDA': 'earnings before interest, taxes, depreciation, and amortization',
-    'YOY': 'year-over-year',
-    'MOM': 'month-over-month',
-    'CAGR': 'compound annual growth rate',
-    'GDP': 'gross domestic product',
-    'ROI': 'return on investment',
-    'ROE': 'return on equity',
-    'EBIT': 'earnings before interest and taxes',
-    'DCF': 'discounted cash flow',
-    'GAAP': 'Generally Accepted Accounting Principles',
-    'LTM': 'last twelve months',
-    'EBIT margin': 'earnings before interest and taxes margin',
-    'EBT': 'earnings before taxes',
-    'EBTA': 'earnings before taxes and amortization',
-    'FTE': 'full-time equivalent',
-    'EBIDTA': 'earnings before interest, depreciation, taxes, and amortization',
-    'EBTIDA': 'earnings before taxes, interest, depreciation, and amortization',
-    'EBITDAR': 'earnings before interest, taxes, depreciation, amortization, and rent',
-    'COGS': 'cost of goods sold',
-    'APR': 'annual percentage rate',
-    'PESTEL': 'Political, Economic, Social, Technological, Environmental, and Legal',
-    'KPI': 'key performance indicator',
-    'SWOT': 'Strengths, Weaknesses, Opportunities, Threats',
-    'CAPEX': 'capital expenditures',
-    'EBITDARM': 'earnings before interest, taxes, depreciation, amortization, rent, and management fees',
-    'EBITDAX': 'earnings before interest, taxes, depreciation, amortization, and exploration expenses',
-    'EBITDAS': 'earnings before interest, taxes, depreciation, amortization, and restructuring costs',
-    'EBITDAX-C': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and commodity derivatives',
-    'EBITDAX-R': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and asset retirement obligations',
-    'EBITDAX-E': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and environmental liabilities'
-        
-        # Add more abbreviations and replacements as needed
+        'P/E': 'price-to-earnings',
+        'EBITDA': 'earnings before interest, taxes, depreciation, and amortization',
+        'YOY': 'year-over-year',
+        'MOM': 'month-over-month',
+        'CAGR': 'compound annual growth rate',
+        'GDP': 'gross domestic product',
+        'ROI': 'return on investment',
+        'ROE': 'return on equity',
+        'EBIT': 'earnings before interest and taxes',
+        'DCF': 'discounted cash flow',
+        'GAAP': 'Generally Accepted Accounting Principles',
+        'LTM': 'last twelve months',
+        'EBIT margin': 'earnings before interest and taxes margin',
+        'EBT': 'earnings before taxes',
+        'EBTA': 'earnings before taxes and amortization',
+        'FTE': 'full-time equivalent',
+        'EBIDTA': 'earnings before interest, depreciation, taxes, and amortization',
+        'EBTIDA': 'earnings before taxes, interest, depreciation, and amortization',
+        'EBITDAR': 'earnings before interest, taxes, depreciation, amortization, and rent',
+        'COGS': 'cost of goods sold',
+        'APR': 'annual percentage rate',
+        'PESTEL': 'Political, Economic, Social, Technological, Environmental, and Legal',
+        'KPI': 'key performance indicator',
+        'SWOT': 'Strengths, Weaknesses, Opportunities, Threats',
+        'CAPEX': 'capital expenditures',
+        'EBITDARM': 'earnings before interest, taxes, depreciation, amortization, rent, and management fees',
+        'EBITDAX': 'earnings before interest, taxes, depreciation, amortization, and exploration expenses',
+        'EBITDAS': 'earnings before interest, taxes, depreciation, amortization, and restructuring costs',
+        'EBITDAX-C': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and commodity derivatives',
+        'EBITDAX-R': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and asset retirement obligations',
+        'EBITDAX-E': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and environmental liabilities'
+    
+    # Add more abbreviations and replacements as needed
     }
     for abbreviation, full_form in replacements.items():
         text = text.replace(abbreviation, full_form)
@@ -249,8 +244,6 @@ def getSentence(listOfSentences,value):
             return sent
     return value
 
-
-
 st.header("Transcript Analysis", divider='rainbow')
 mainTranscript = st.text_area("Enter the transcript:", height=100)
 doc = nlp(mainTranscript)
@@ -304,15 +297,14 @@ if st.button("Analyze"):
     expences=[x["expense"] for x in ner_result if "expense" in x]
     for idx in range(len(revenues)): 
          st.text_input(f'Revenue:{idx+1}', revenues[idx])
-         st.text_input(f'Sentences', getSentence(sentences,revenues[idx]),key=idx**2)
+         st.text_input(f'Revenue-Sentence:{idx+1}', getSentence(sentences,revenues[idx]))
     for idx in range(len(profits)):
          st.text_input(f'Profit:{idx+1}', profits[idx])
-         st.text_input(f'Sentences', getSentence(sentences,profits[idx]),key=idx**3)
+         st.text_input(f'Profit-Sentence:{idx+1}', getSentence(sentences,profits[idx]))
     for idx in range(len(expences)):
          st.text_input(f'Expences:{idx+1}', expences[idx])
-         st.text_input(f'Sentences', getSentence(sentences,expences[idx]),key=idx**4)
+         st.text_input(f'Expences-Sentences:{idx+1}', getSentence(sentences,expences[idx]))
 
-    
     st.subheader("Investment Recommendation", divider='rainbow')
     profitAmount=sum([convert_amount_to_number(x) for x in profits])
     expencesAmount=sum([convert_amount_to_number(x) for x in expences])
@@ -320,6 +312,3 @@ if st.button("Analyze"):
         st.markdown(f'<span style="color:green">{"This is a great chance for investment. Do consider it."}</span>', unsafe_allow_html=True)
     else:
         st.markdown(f'<span style="color:red">{"Not the best chance for investment."}</span>', unsafe_allow_html=True)
-
-
-