Create app.py

app.py

@@ -0,0 +1,94 @@
+import openai
+import gradio as gr
+import pandas as pd
+import sqlite3
+import os
+openai.api_key = os.environ["Secret"]
+
+#OpenAi call
+def gpt3(texts):
+    response = openai.Completion.create(
+      engine="code-davinci-002",
+      prompt= texts,
+          temperature=0,
+          max_tokens=750,
+          top_p=1,
+          frequency_penalty=0.0,
+          presence_penalty=0.0,
+          stop = (";", "/*", "</code>")
+    )
+    x = response.choices[0].text 
+    
+    return x
+
+# Function to elicit sql response from model
+
+
+
+# Function to elicit sql response from model
+
+
+def greet(prompt, file = None):
+
+    #get the file path from the file object
+    file_path = file.name
+
+    # read the file and get the column names
+    if file_path:
+        if file_path.endswith(".csv"):
+            df = pd.read_csv(file_path)
+            columns = " ".join(df.columns)
+            
+
+
+
+        elif file_path.endswith((".xls", ".xlsx")):
+            df = pd.read_excel(file_path)
+            columns = " ".join(df.columns)
+        else:
+            return "Invalid file type. Please provide a CSV or Excel file."
+        
+        # create a SQLite database in memory
+        con = sqlite3.connect(":memory:")
+        # extract the table name so it can be used in the SQL query
+        # in order to get the table name, we need to remove the file extension
+
+        table_name = 
+
+
+
+        
+
+
+
+        # write the DataFrame to a SQL table
+
+
+
+        df.to_sql(table_name, con)
+    else:
+        return "Please upload a file."
+    txt= (f'''/*Prompt: {prompt}\nColumns: {columns}\nTable: {table_name}*/ \n —-SQL Code:\n''')
+    sql = gpt3(txt)
+    
+    
+    # execute the SQL query
+    if con:
+        df = pd.read_sql_query(sql, con)
+        return sql, df
+    else:
+        return sql, None
+        
+
+
+
+
+
+
+#Code to set up Gradio UI
+iface = gr.Interface(greet, 
+                     inputs = ["text", ("file")], 
+                     outputs = ["text",gr.Dataframe(type="pandas")],
+                     title="Natural Language to SQL", 
+                     description="Enter any prompt and get a SQL statement back! For better results, give it more context")
+iface.launch()