Update app.py

app.py

@@ -9,7 +9,9 @@ import logging
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import mean_squared_error, r2_score
-import statsmodels.api as sm
+import statsmodels.api as sm  # For time series analysis
+from sklearn.metrics.pairwise import cosine_similarity  # For recommendations
+
 
 # Configure logging
 logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -64,33 +66,11 @@ Instructions:
 
 - If the question involves data retrieval or simple aggregations, generate a SQL query.
 - If the question requires statistical analysis or time series analysis, generate a Python code snippet using pandas, numpy, and statsmodels.
-- If the question involves predictions or modeling, generate a Python code snippet using scikit-learn.
+- If the question involves predictions, modeling, or recommendations, generate a Python code snippet using scikit-learn or pandas.
 - Ensure that you only use the columns provided.
 - Do not include any import statements in the code.
 - Provide the code between <CODE> and </CODE> tags.
 
-Examples:
-
-User Query: Calculate the average sales.
-Response:
-<CODE>
-result = data['Sales'].mean()
-</CODE>
-
-User Query: Total sales for Product A
-Response:
-<CODE>
-SELECT SUM(Sales) AS "Total sales" FROM {table_name} WHERE Product = 'Product A' COLLATE NOCASE
-</CODE>
-
-User Query: Show me the sales trend over time.
-Response:
-<CODE>
-data['Date'] = pd.to_datetime(data['Date'])
-sales_by_date = data.groupby('Date')['Sales'].sum().reset_index()
-result = sales_by_date
-</CODE>
-
 Question: {question}
 
 Table name: {table_name}
@@ -100,6 +80,9 @@ Valid columns: {columns}
 Response:
 """
 
+
+
+
 prompt = PromptTemplate(template=template, input_variables=['question', 'table_name', 'columns'])
 
 # Set up the LLM Chain
@@ -140,8 +123,10 @@ def execute_code(code):
             'train_test_split': train_test_split,
             'mean_squared_error': mean_squared_error,
             'r2_score': r2_score,
-            'sm': sm
+            'sm': sm,  # Added statsmodels
+            'cosine_similarity': cosine_similarity  # Added cosine_similarity
         }
+
         exec(code, {}, local_vars)
         result = local_vars.get('result')
         return result
@@ -214,3 +199,7 @@ for message in st.session_state.history:
             st.markdown(f"**Assistant:** {content}")
         else:
             st.markdown(f"**Assistant:** {content}")
+
+# Place the text input after displaying the conversation
+st.text_input("Enter your question:", key='user_input', on_change=process_input)
+