Update chat.py

chat.py

@@ -89,9 +89,10 @@ class ChatHandler:
 
             # Define the prompt template
             template_query_generation = """
-            Based on the table schema below, write a SQL query that would answer the user's question.
+            
+            Based on the table schema below, write a mySQL query with correct syntax that would answer the user's question.
             Only write the SQL query without explanations.
-
+            
             Schema:
             {schema}
 
@@ -123,7 +124,7 @@ class ChatHandler:
                 
             except Exception as e:
                 print(f"Error executing SQL query: {str(e)}")
-                return "Sorry, I couldn't process your request. Please try after some time."
+                return f"As you know I am still learning at this moment I am not able to respond to your question.\nThank you for your patience!"
 
             # If no result, return an appropriate message
             if not result:
@@ -134,7 +135,7 @@ class ChatHandler:
 
         except Exception as e:
             print( f"Error fetching schema details or processing query: {str(e)}")
-            return "Sorry, I couldn't process your request. Please try after some time."
+            return f"As you know I am still learning at this moment I am not able to respond to your question.\nThank you for your patience!"
 
     def answer_question(self, query, visual_query):
         global global_df
@@ -153,26 +154,29 @@ class ChatHandler:
               {
                   "name": "Database Schema",
                   "function": lambda q: self._load_schema_from_database(q),
-                  "description": """Search within the database schema and generate SQL-based responses. 
-                  The database has single table 'tp_material_forecast' which contains the columns 'date', 'material_name', 'material_quantity', and 'type'. If the material name is given,  frame the query in such a way that the material_name is not case-sensitive. If the material name is not present in the table, return the proper message as "This material name is not in the database".
-                 display the response as a combination of response summary and the response data in the form of table. If the response has month column, display the month as name For example, January instead of displaying as 1.
-                 If the user requested comparison between two or more years or the user asks for the data for all years, data should be shown for all the years with month as first column and the years like 2020, 2021 etc as the adjacent columns. Do not show everything in the same column. (For example, if the user requested from 2020 to 2024, then display the output table with the columns [Month, Material value in 2020, Material value in 2020, Material value in 2021, Material value in 2022, Material value in 2023, Material value in 2024]) so that the records will be displayed for all the months from Jaunary to December across the years. 
-                  include the table data in the Final answer.""",
+                  "description": """Search within the mysql database schema and generate SQL-based responses. write mySQL query with correct syntax.
+                  The database has single table 'tp_material_forecast' which contains the columns 'date', 'material_name', 'material_quantity', and 'type'. Frame the query only with these four columns.
+                  If the material name is given,  frame the query in such a way that the material_name is not case-sensitive. 
+                  If the material name is not present in the table, return the proper message as "This material name is not in the database". Do not give any false values if the material name is not available in database.
+                  display the response as a combination of response summary and the response data in the form of table. If the response has month column, display the month as name For example, January instead of displaying as 1.
+                  If the user requested comparison between two or more years or the user asks for the data for all years, data should be shown for all the years with month as first column and the years like 2020, 2021 etc as the adjacent columns. 
+                  Do not show everything in the same column. (For example, if the user requested from 2020 to 2024, then display the output table with the columns [Month, Material value in 2020, Material value in 2020, Material value in 2021, Material value in 2022, Material value in 2023, Material value in 2024]) so that the records will be displayed for all the months from Jaunary to December across the years. 
+                  Always display the table data at the end in the Final answer even if it has a single value. If there is any error while executing the user question, kindly display the error message as 'As you know I am still learning at this moment I am not able to respond to your question.\nThank you for your patience!'""",
               },
         ]
 
         agent_prompt = PromptTemplate(
             input_variables=["input", "agent_scratchpad"],
             template="""
-                        You are a highly skilled AI assistant specializing in document analysis.
-                        I have uploaded a document containing material demand forecasts with columns for 'date', 'Material Name', 'Material Quantity', and 'Type'.
+                        You are a highly skilled AI assistant specializing in mysql database.
+                        I have a mysql database for material demand forecasts with columns as 'date', 'material_name', 'material_quantity', and 'type'.
                         
                         The data includes historical demand information for various items.
 
                         1. The uploaded document includes:
                            - **Date:** The date of demand entry.
                            - **Material Name:** The name of the material or equipment.
-                           - **Material Quantity:** The number of units Utilized or forecasted.
+                           - **Material Quantity:** The number of units actual or predicted.
                            - **Type:** Type contains actual or forecasted, actual represents the actual material utilized and forecasted represents the prediction by ai model.
 
                         2. I may ask questions such as:
@@ -182,12 +186,13 @@ class ChatHandler:
                            - Comparing demand values between two or more items.
 
                         Your task:
-                        - If the query relates to forecasting or involves the uploaded document, extract the necessary information from it 
+                        - If the query relates to forecasting, extract the necessary information from it 
                           and provide precise, professional, and data-driven responses.
                         
                         Make sure your answers are aligned with the uploaded document, depending on the context of the query.
-                         display the response as mentioned in the tool description.  display the output table whereever it is required.
-                         include the table data in the Final answer if it is there.
+                         display the response as mentioned in the tool description.
+                         include the table data in the Final answer even if there is a single value.
+                         Do not display the first line and the last line of the table as '''
                         Tools available to you:
                         {tools}
 
@@ -249,7 +254,9 @@ class ChatHandler:
                 visual_response = None
             return response, visual_response
         except Exception as e:
-            return f"Error while processing your query: {str(e)}", None
+            print(f"Error while processing your query: {str(e)}")
+            return f"As you know I am still learning at this moment I am not able to respond to your question.\nThank you for your patience!" , None
+            
 
 
 
@@ -259,7 +266,12 @@ def create_visualization_csv(visual_query):
     #import matplotlib
     #matplotlib.use('TkAgg')  # Replace with 'QtAgg' or 'MacOSX' if on macOS
 
-    visual_query = visual_query + "  create chart with suitable x and y axis as user requested. use proper axis values. Do not miss any values.  add legend in the chart.  mention axis labels in the chart. mention only month name in date axis and not the numbers."
+    visual_query = visual_query + """  create chart with suitable x and y axis as user requested. 
+                                        use proper axis scale and mention axis values properly. 
+                                        Do not miss any values. 
+                                        Mention only month name in date axis and not the numbers or the date.
+                                        Do not place legend in the middle of the chart. Place the legend in such a way that the plotted chart is not hidden.
+                                        Return the image path only after plotting all the values."""
 
     
     llm_chart = OpenAI()
@@ -270,7 +282,7 @@ def create_visualization_csv(visual_query):
     sdf = SmartDataframe(global_df, config={"llm": llm_chart})
     llm_response = sdf.chat(visual_query)
     if "no result" in llm_response:
-        return " There is a problem in generating the chart. Please try again ater some time."
+        return f"As you know I am still learning at this moment I am not able to respond to your question.\nThank you for your patience!"
     return llm_response