Update app.py

app.py

@@ -100,7 +100,7 @@ def get_summarization(client:groq.Groq,
                       '''.format(user_question = use_question, df = df)
                       return chat_with_groq(client,prompt,model,None)
 
-import pandas as pd
+
 import re
 from datetime import datetime
 import numpy as np
@@ -307,15 +307,15 @@ def upload_file(files) -> List[str]:
 
 def user_prompt_sanitization(user_prompt:str)->str:
   guide = """
-          You are a Groq advisor what you are to do is to collect user prompts and with the available sql tables availables tailor the question to partain to the data 
-          The tables desctiptions provided below will be what you'd need to look at in order to sanitize the user's prompt. 
+          You are a Groq advisor what you are to do is to collect user prompts and with the available sql tables availables tailor the question to partain to the data
+          The tables desctiptions provided below will be what you'd need to look at in order to sanitize the user's prompt.
           {table_description}
-          Here are some organization tips for your queries. 
+          Here are some organization tips for your queries.
           * When an id is requested but not explicitely defined make sure you look at the table above and reference a proper id which will be then proper
           * An example includes "get the amount of the purchase with the id 5" sample_response: "get the purchase with the purchase_id 5"
           * In the case of numeric or quantifiable attributes such "get the top 3 purchases" sample_response: "get the 3 purchases with the highest amount"
           * Ensure that you do not query a table that does not exist
-           
+
           Question:
           --------
           {user_question}
@@ -358,6 +358,8 @@ def queryModel(user_prompt:str,model:str = "llama3-70b-8192",api_key:str=userdat
       )]
 
     fotmatted_sql_query = sqlparse.format(sql_query, reindent=True, keyword_case='upper')
+    # print(f"SQL Query: {fotmatted_sql_query}")
+    # print(results_df.to_markdown())
     query_n_results = "SQL Query: " + fotmatted_sql_query + "\n\n" + results_df.to_markdown()
     summarization = get_summarization(client,user_prompt,results_df,model)
     query_n_results += "\n\n" + summarization
@@ -396,5 +398,3 @@ with gr.Blocks() as demo:
 
 
 demo.launch(share=True)
-
-