Update app.py

app.py

@@ -152,134 +152,12 @@ def calculations():
             st.success(f"Total (A): {total_A:.2f}")
             st.success(f"Creditors (After Margin): {total_B:.2f}")
             st.success(f"Drawing Power (DP): {dp:.2f}")
-
-def run_model1_chat():
-    st.header("Model 1 Chat Interface")
-    if 'chat_history' not in st.session_state:
-        st.session_state['chat_history'] = []
-    user_input = st.text_input("You:", key="model1_input")
-    if st.button("Send", key='model1_send'):
-        if user_input:
-            st.session_state.chat_history.append(("User", user_input))
-            try:
-                chat_completion = client.chat.completions.create(
-                    messages=[
-                        {'role': 'user', 'content': user_input}
-                    ],
-                    model="gemma2-9b-it",
-                    stream=False,
-                    temperature=0.0
-                )
-                response = chat_completion.choices[0].message.content.strip()
-                st.session_state.chat_history.append(("Model", response))
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
-                st.error("Please check your API key and model availability.")
-    for speaker, message in st.session_state.chat_history:
-        if speaker == "User":
-            st.markdown(f"**You:** {message}")
-        else:
-            st.markdown(f"**Model 1:** {message}")
-
-
-def retrieve_relevant_financial_statements(query, index, statements, model, top_k=10, max_tokens=1500):
-    query_embedding = model.encode([query], convert_to_numpy=True)
-    distances, indices = index.search(query_embedding.astype('float32'), top_k)
-    retrieved_statements = []
-    total_tokens = 0
-    for idx in indices[0]:
-        statement = statements[idx]['statement']
-        token_count = len(statement.split())
-        if total_tokens + token_count > max_tokens:
-            break
-        retrieved_statements.append(statements[idx])
-        total_tokens += token_count
-    return retrieved_statements
-
-
-def model2_financial_data():
-    st.header("Financial Data Assistant (Model 2)")
-    financial_index_path = os.path.join( 'financial_index.faiss')
-    financial_statements_path = os.path.join( 'financial_statements.pkl')
-    if not os.path.exists(financial_index_path):
-        st.error("Financial FAISS index not found.")
-        st.stop()
-    financial_index = faiss.read_index(financial_index_path)
-    if not os.path.exists(financial_statements_path):
-        st.error("Financial statements data not found.")
-        st.stop()
-    with open(financial_statements_path, 'rb') as f:
-        financial_statements = pickle.load(f)
-    user_query = st.text_area("Ask a question about Indian state-wise financial details (1980-2015):", key='model2_input')
-
-    if st.button("Get Answer", key='model2_button'):
-        if user_query:
-            import re
-            metrics_list = [
-                'aggregate expenditure', 'capital expenditure', 'gross fiscal deficits',
-                'nominal gsdp series', 'own tax revenues', 'revenue deficits',
-                'revenue expenditure', 'social sector expenditure'
-            ]
-            metrics_pattern = '|'.join(metrics_list)
-            metric_regex = re.compile(rf'\b({metrics_pattern})\b', re.IGNORECASE)
-            metric_match = metric_regex.search(user_query)
-            if metric_match:
-                query_metric = metric_match.group(1).strip().title()
-            else:
-                query_metric = None
-            states_list = list(set(s['state'] for s in financial_statements))
-            states_pattern = '|'.join(states_list)
-            state_regex = re.compile(rf'\b({states_pattern})\b', re.IGNORECASE)
-            state_match = state_regex.search(user_query)
-            if state_match:
-                query_state = state_match.group(1).strip()
-            else:
-                query_state = None
-            year_regex = re.compile(r'(\d{4}(?:-\d{2})?)')
-            year_match = year_regex.search(user_query)
-            if year_match:
-                query_year = year_match.group(1)
-                if len(query_year) == 4:
-                    query_year = f"{query_year}-{str(int(query_year[-2:])+1).zfill(2)}"
-                elif len(query_year) == 7:
-                    pass
-            else:
-                query_year = None
-            if query_state and query_year:
-                data = {}
-                for s in financial_statements:
-                    if (
-                        s['state'].lower() == query_state.lower() and
-                        s['year'] == query_year
-                    ):
-                        if query_metric:
-                            if s['metric_type'].lower() == query_metric.lower():
-                                data[s['metric_type']] = s['value']
-                                break  
-                        else:
-                            data[s['metric_type']] = s['value']
-                if data:
-                    if query_metric:
-                        value = data.get(query_metric)
-                        if value is not None:
-                            st.write(f"The {query_metric} of {query_state} in {query_year} is {value}")
-                        else:
-                            st.write(f"{query_metric} data not found for {query_state} in {query_year}.")
-                    else:
-                        st.write(f"Financial data for **{query_state}** in **{query_year}**:")
-                        df = pd.DataFrame(list(data.items()), columns=['Metric', 'Value'])
-                        st.table(df)
-                else:
-                    st.write("Data not found for the specified state, year, or metric.")
-            else:
-                st.write("Could not understand the query. Please specify the state and year.")
-                
 def main():
     st.set_page_config(page_title="Finance Assistant", page_icon="💸", layout="wide")
     st.title("💸 Finance Assistant")
     option = st.radio(
         "Choose a Functionality",
-        ("Calculation Methodology", "Circular Compliance", "Industry Classification", "Model 1", "Model 2")
+        ("Calculation Methodology", "Circular Compliance", "Industry Classification")
     )
     if option == "Calculation Methodology":
         calculations()
@@ -287,9 +165,5 @@ def main():
         circular_compliance()
     elif option == "Industry Classification":
         industry_classification()
-    elif option == "Model 1":
-        run_model1_chat()
-    elif option == "Model 2":
-        model2_financial_data()
 if __name__ == "__main__":
     main()