Update app.py

app.py

@@ -1,374 +1,295 @@
-import os
-import streamlit as st
-import pickle
-import faiss
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-from groq import Groq
-
-# Set your Groq API Key (use environment variable for security)
-GROQ_API_KEY = "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"  # Ensure this environment variable is set
-client = Groq(api_key=GROQ_API_KEY)
-
-# Load the embedding model
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Paths to your assets folder
-assets_folder = os.path.join(os.getcwd(), 'assets')
-
-# Function to load resources from local storage
-def load_resources():
-    # Paths to index and chunk files
-    industry_index_path = os.path.join( 'industry_index.faiss')
-    industry_chunks_path = os.path.join( 'industry_chunks.pkl')
-    circular_index_path = os.path.join( 'circular_index.faiss')
-    circular_chunks_path = os.path.join( 'circular_chunks.pkl')
-
-    # Check if the files exist
-    if not all(os.path.exists(path) for path in [industry_index_path, industry_chunks_path, circular_index_path, circular_chunks_path]):
-        st.error("FAISS indexes and chunk files not found in the assets folder. Please ensure they are present.")
-        st.stop()
-
-    # Load FAISS indexes and chunks
-    industry_index = faiss.read_index(industry_index_path)
-    with open(industry_chunks_path, 'rb') as f:
-        industry_chunks = pickle.load(f)
-    circular_index = faiss.read_index(circular_index_path)
-    with open(circular_chunks_path, 'rb') as f:
-        circular_chunks = pickle.load(f)
-    return industry_index, industry_chunks, circular_index, circular_chunks
-
-# Prepare data
-industry_index, industry_chunks, circular_index, circular_chunks = load_resources()
-
-# Function to retrieve relevant chunks
-def retrieve_relevant_chunks(query, index, chunks, top_k=5):
-    query_embedding = model.encode([query], convert_to_numpy=True)
-    distances, indices = index.search(query_embedding, top_k)
-    retrieved_chunks = [chunks[i] for i in indices[0]]
-    return retrieved_chunks
-
-# Function for Circular Compliance (Problem Statement 2)
-def circular_compliance():
-    st.header("Circular Compliance Assistant")
-    user_query = st.text_area("Enter your scenario or question:", key='circular_input')
-    if st.button("Check Compliance", key='circular_button'):
-        if user_query:
-            relevant_chunks = retrieve_relevant_chunks(user_query, circular_index, circular_chunks)
-            context = "\n".join(relevant_chunks)
-            prompt =  f"""
-You are an expert RBI compliance analyst. Based on the provided RBI Master Circular on Management of Advances:
-
-{context}
-
-Please analyze the following scenario for compliance:
-{user_query}
-
-Provide a detailed compliance analysis with the following structure:
-
-1. Compliance Status:
-- Clear statement whether the scenario is compliant or non-compliant
-- Level of certainty in the assessment
-
-2. Relevant Circular Details:
-- Specific section(s) and paragraph references
-- Direct quotes from applicable sections where relevant
-
-3. Detailed Analysis:
-- Breakdown of key compliance requirements
-- Calculation/numerical analysis if applicable
-- Specific points of compliance/non-compliance
-
-4. Additional Considerations:
-- Related requirements or obligations
-- Monitoring/reporting requirements if applicable
-
-5. Recommendation:
-- Clear guidance on what needs to be done for compliance
-- Specific steps to address any non-compliance
-
-Please provide definitive guidance based solely on the circular content, avoiding ambiguity or speculation.
-
-Response:
-"""
-            chat_completion = client.chat.completions.create(
-                messages=[
-                    {'role': 'user', 'content': prompt}
-                ],
-                model="gemma2-9b-it",
-                stream=False,
-                temperature=0.0
-            )
-            response = chat_completion.choices[0].message.content.strip()
-            st.write(response)
-
-# Function for Industry Classification (Problem Statement 3)
-def industry_classification():
-    st.header("Industry Classification Assistant")
-    user_keywords = st.text_input("Enter keywords related to the industry:", key='industry_input')
-    if st.button("Get Industry Classification", key='industry_button'):
-        if user_keywords:
-            relevant_chunks = retrieve_relevant_chunks(user_keywords, industry_index, industry_chunks)
-            context = "\n".join(relevant_chunks)
-            prompt = f"""
-You are an assistant helping to classify industries based on keywords. Based on the following information:
-
-{context}
-
-User's Keywords:
-{user_keywords}
-
-Suggest the most appropriate industry classification codes. Ask any necessary follow-up questions to clarify if needed.
-
-Answer:
-"""
-            chat_completion = client.chat.completions.create(
-                messages=[
-                    {'role': 'user', 'content': prompt}
-                ],
-                model="gemma2-9b-it",
-                stream=False,
-                temperature=0.0
-            )
-            response = chat_completion.choices[0].message.content.strip()
-            st.write(response)
-
-# Existing calculation function (Problem Statement 1)
-def calculations():
-    st.subheader("Calculation Methodology")
-    calc_option = st.selectbox("Choose Calculation Method",
-                             ("Maximum Permissible Bank Finance (MPBF)", "Drawing Power (DP)"))
-
-    if calc_option == "Maximum Permissible Bank Finance (MPBF)":
-        st.header("MPBF Calculation")
-        total_current_assets = st.number_input("Total Current Assets (TCA):", min_value=0.0, value=0.0)
-        other_current_liabilities = st.number_input("Other Current Liabilities (OCL):", min_value=0.0, value=0.0)
-        actual_nwc = st.number_input("Actual/Projected Net Working Capital (NWC):", min_value=0.0, value=0.0)
-
-        if st.button("Calculate MPBF"):
-            working_capital_gap = total_current_assets - other_current_liabilities
-            minimum_stipulated_nwc = 0.25 * total_current_assets
-            item_6 = working_capital_gap - minimum_stipulated_nwc
-            item_7 = working_capital_gap - actual_nwc
-            mpbf = min(item_6, item_7)
-
-            st.success(f"Working Capital Gap (WCG): {working_capital_gap:.2f}")
-            st.success(f"Minimum Stipulated NWC (25% of TCA): {minimum_stipulated_nwc:.2f}")
-            st.success(f"Item 6 (WCG - Minimum Stipulated NWC): {item_6:.2f}")
-            st.success(f"Item 7 (WCG - Actual NWC): {item_7:.2f}")
-            st.success(f"Maximum Permissible Bank Finance (MPBF): {mpbf:.2f}")
-
-    elif calc_option == "Drawing Power (DP)":
-        st.header("DP Calculation")
-        inventory_margin = 0.25
-        receivables_margin = 0.40
-        creditors_margin = 0.40
-
-        st.subheader("Inventory Details")
-        raw_material = st.number_input("Raw Material:", min_value=0.0, value=0.0)
-        consumable_spares = st.number_input("Other Consumable Spares:", min_value=0.0, value=0.0)
-        stock_in_process = st.number_input("Stock-in-process:", min_value=0.0, value=0.0)
-        finished_goods = st.number_input("Finished Goods:", min_value=0.0, value=0.0)
-
-        st.subheader("Receivables")
-        domestic_receivables = st.number_input("Domestic Receivables:", min_value=0.0, value=0.0)
-        export_receivables = st.number_input("Export Receivables:", min_value=0.0, value=0.0)
-
-        st.subheader("Creditors")
-        creditors = st.number_input("Creditors:", min_value=0.0, value=0.0)
-
-        if st.button("Calculate DP"):
-            inventory_total = raw_material + consumable_spares + stock_in_process + finished_goods
-            inventory_advance = inventory_total * (1 - inventory_margin)
-            receivables_total = domestic_receivables + export_receivables
-            receivables_advance = receivables_total * (1 - receivables_margin)
-            creditors_advance = creditors * (1 - creditors_margin)
-            total_A = inventory_advance + receivables_advance
-            total_B = creditors_advance
-            dp = total_A - total_B
-
-            st.success(f"Total Inventory (After Margin): {inventory_advance:.2f}")
-            st.success(f"Total Receivables (After Margin): {receivables_advance:.2f}")
-            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}")
-
-# Function for Model 1 chat interface
-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:
-                # Get model response
-                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.")
-
-    # Display chat history
-    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)")
-
-    # Load the FAISS index and financial statements
-    financial_index_path = os.path.join( 'financial_index.faiss')
-    financial_statements_path = os.path.join( 'financial_statements.pkl')
-
-    # Load FAISS index
-    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)
-
-    # Load statements
-    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)
-
-    # Allow the user to input a query
-    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:
-            # Extract metric, state, and year from the user's query
-            import re
-
-            # List of possible metrics
-            metrics_list = [
-                'aggregate expenditure', 'capital expenditure', 'gross fiscal deficits',
-                'nominal gsdp series', 'own tax revenues', 'revenue deficits',
-                'revenue expenditure', 'social sector expenditure'
-            ]
-
-            # Create a pattern to match any of the metrics
-            metrics_pattern = '|'.join(metrics_list)
-            metric_regex = re.compile(rf'\b({metrics_pattern})\b', re.IGNORECASE)
-
-            # Extract metric
-            metric_match = metric_regex.search(user_query)
-            if metric_match:
-                query_metric = metric_match.group(1).strip().title()
-            else:
-                query_metric = None
-
-            # Extract state
-            # Assuming state names are capitalized properly in the data
-            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
-
-            # Extract year
-            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)
-                # Normalize the year format if needed
-                if len(query_year) == 4:
-                    # Convert "1992" to "1992-93"
-                    query_year = f"{query_year}-{str(int(query_year[-2:])+1).zfill(2)}"
-                elif len(query_year) == 7:
-                    # Already in "1992-93" format
-                    pass
-            else:
-                query_year = None
-
-            if query_state and query_year:
-                # Collect data based on the extracted information
-                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  # Since we found the specific metric, we can stop
-                        else:
-                            data[s['metric_type']] = s['value']
-
-                if data:
-                    if query_metric:
-                        # Display only the specific 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:
-                        # Display all metrics
-                        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")
-    )
-
-    if option == "Calculation Methodology":
-        calculations()
-    elif option == "Circular Compliance":
-        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()
+import os
+import streamlit as st
+import pickle
+import faiss
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+GROQ_API_KEY = "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" 
+client = Groq(api_key=GROQ_API_KEY)
+model = SentenceTransformer('all-MiniLM-L6-v2')
+assets_folder = os.path.join(os.getcwd(), 'assets')
+
+def load_resources():
+    industry_index_path = os.path.join( 'industry_index.faiss')
+    industry_chunks_path = os.path.join( 'industry_chunks.pkl')
+    circular_index_path = os.path.join( 'circular_index.faiss')
+    circular_chunks_path = os.path.join( 'circular_chunks.pkl')
+    if not all(os.path.exists(path) for path in [industry_index_path, industry_chunks_path, circular_index_path, circular_chunks_path]):
+        st.error("FAISS indexes and chunk files not found in the assets folder. Please ensure they are present.")
+        st.stop()
+    industry_index = faiss.read_index(industry_index_path)
+    with open(industry_chunks_path, 'rb') as f:
+        industry_chunks = pickle.load(f)
+    circular_index = faiss.read_index(circular_index_path)
+    with open(circular_chunks_path, 'rb') as f:
+        circular_chunks = pickle.load(f)
+    return industry_index, industry_chunks, circular_index, circular_chunks
+industry_index, industry_chunks, circular_index, circular_chunks = load_resources()
+
+def retrieve_relevant_chunks(query, index, chunks, top_k=5):
+    query_embedding = model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_embedding, top_k)
+    retrieved_chunks = [chunks[i] for i in indices[0]]
+    return retrieved_chunks
+    
+def circular_compliance():
+    st.header("Circular Compliance Assistant")
+    user_query = st.text_area("Enter your scenario or question:", key='circular_input')
+    if st.button("Check Compliance", key='circular_button'):
+        if user_query:
+            relevant_chunks = retrieve_relevant_chunks(user_query, circular_index, circular_chunks)
+            context = "\n".join(relevant_chunks)
+            prompt =  f"""
+You are an expert RBI compliance analyst. Based on the provided RBI Master Circular on Management of Advances:
+{context}
+Please analyze the following scenario for compliance:
+{user_query}
+Provide a detailed compliance analysis with the following structure:
+1. Compliance Status:
+- Clear statement whether the scenario is compliant or non-compliant
+- Level of certainty in the assessment
+2. Relevant Circular Details:
+- Specific section(s) and paragraph references
+- Direct quotes from applicable sections where relevant
+3. Detailed Analysis:
+- Breakdown of key compliance requirements
+- Calculation/numerical analysis if applicable
+- Specific points of compliance/non-compliance
+4. Additional Considerations:
+- Related requirements or obligations
+- Monitoring/reporting requirements if applicable
+5. Recommendation:
+- Clear guidance on what needs to be done for compliance
+- Specific steps to address any non-compliance
+Please provide definitive guidance based solely on the circular content, avoiding ambiguity or speculation.
+Response:
+"""
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {'role': 'user', 'content': prompt}
+                ],
+                model="gemma2-9b-it",
+                stream=False,
+                temperature=0.0
+            )
+            response = chat_completion.choices[0].message.content.strip()
+            st.write(response)
+
+def industry_classification():
+    st.header("Industry Classification Assistant")
+    user_keywords = st.text_input("Enter keywords related to the industry:", key='industry_input')
+    if st.button("Get Industry Classification", key='industry_button'):
+        if user_keywords:
+            relevant_chunks = retrieve_relevant_chunks(user_keywords, industry_index, industry_chunks)
+            context = "\n".join(relevant_chunks)
+            prompt = f"""
+You are an assistant helping to classify industries based on keywords. Based on the following information:
+{context}
+User's Keywords:
+{user_keywords}
+Suggest the most appropriate industry classification codes. Ask any necessary follow-up questions to clarify if needed.
+Answer:
+"""
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {'role': 'user', 'content': prompt}
+                ],
+                model="gemma2-9b-it",
+                stream=False,
+                temperature=0.0
+            )
+            response = chat_completion.choices[0].message.content.strip()
+            st.write(response)
+
+def calculations():
+    st.subheader("Calculation Methodology")
+    calc_option = st.selectbox("Choose Calculation Method",
+                             ("Maximum Permissible Bank Finance (MPBF)", "Drawing Power (DP)"))
+    if calc_option == "Maximum Permissible Bank Finance (MPBF)":
+        st.header("MPBF Calculation")
+        total_current_assets = st.number_input("Total Current Assets (TCA):", min_value=0.0, value=0.0)
+        other_current_liabilities = st.number_input("Other Current Liabilities (OCL):", min_value=0.0, value=0.0)
+        actual_nwc = st.number_input("Actual/Projected Net Working Capital (NWC):", min_value=0.0, value=0.0)
+        if st.button("Calculate MPBF"):
+            working_capital_gap = total_current_assets - other_current_liabilities
+            minimum_stipulated_nwc = 0.25 * total_current_assets
+            item_6 = working_capital_gap - minimum_stipulated_nwc
+            item_7 = working_capital_gap - actual_nwc
+            mpbf = min(item_6, item_7)
+            st.success(f"Working Capital Gap (WCG): {working_capital_gap:.2f}")
+            st.success(f"Minimum Stipulated NWC (25% of TCA): {minimum_stipulated_nwc:.2f}")
+            st.success(f"Item 6 (WCG - Minimum Stipulated NWC): {item_6:.2f}")
+            st.success(f"Item 7 (WCG - Actual NWC): {item_7:.2f}")
+            st.success(f"Maximum Permissible Bank Finance (MPBF): {mpbf:.2f}")
+    elif calc_option == "Drawing Power (DP)":
+        st.header("DP Calculation")
+        inventory_margin = 0.25
+        receivables_margin = 0.40
+        creditors_margin = 0.40
+        st.subheader("Inventory Details")
+        raw_material = st.number_input("Raw Material:", min_value=0.0, value=0.0)
+        consumable_spares = st.number_input("Other Consumable Spares:", min_value=0.0, value=0.0)
+        stock_in_process = st.number_input("Stock-in-process:", min_value=0.0, value=0.0)
+        finished_goods = st.number_input("Finished Goods:", min_value=0.0, value=0.0)
+        st.subheader("Receivables")
+        domestic_receivables = st.number_input("Domestic Receivables:", min_value=0.0, value=0.0)
+        export_receivables = st.number_input("Export Receivables:", min_value=0.0, value=0.0)
+        st.subheader("Creditors")
+        creditors = st.number_input("Creditors:", min_value=0.0, value=0.0)
+        if st.button("Calculate DP"):
+            inventory_total = raw_material + consumable_spares + stock_in_process + finished_goods
+            inventory_advance = inventory_total * (1 - inventory_margin)
+            receivables_total = domestic_receivables + export_receivables
+            receivables_advance = receivables_total * (1 - receivables_margin)
+            creditors_advance = creditors * (1 - creditors_margin)
+            total_A = inventory_advance + receivables_advance
+            total_B = creditors_advance
+            dp = total_A - total_B
+            st.success(f"Total Inventory (After Margin): {inventory_advance:.2f}")
+            st.success(f"Total Receivables (After Margin): {receivables_advance:.2f}")
+            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")
+    )
+    if option == "Calculation Methodology":
+        calculations()
+    elif option == "Circular Compliance":
+        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()