svp

README.md

@@ -4,9 +4,9 @@ emoji: 🐨
 colorFrom: green
 colorTo: purple
 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 3.0.6
 app_file: app.py
 pinned: true
 ---
 
-Natural Language to SQL
+Natural Language to SQL

app.py

@@ -1,32 +1,31 @@
-import os
-from openai import OpenAI
+import openai
 import gradio as gr
+import os
 
-client = OpenAI(api_key=os.environ["Secret"])
-
+#OpenAi call
 def gpt3(texts):
-    response = client.chat.completions.create(
-        model="gpt-4",
-        messages=[{"role": "user", "content": texts}],
-        temperature=0,
-        max_tokens=750,
-        top_p=1,
-        frequency_penalty=0.0,
-        presence_penalty=0.0,
-        stop=[";", "*/", "</code>"]
+    openai.api_key = os.environ["Secret"]
+    response = openai.Completion.create(
+      engine="text-davinci-003",
+      prompt= texts,
+          temperature=0,
+          max_tokens=750,
+          top_p=1,
+          frequency_penalty=0.0,
+          presence_penalty=0.0,
+          stop = (";", "/*", "</code>")
     )
-    return response.choices[0].message.content
+    x = response.choices[0].text 
+    
+    return x
 
+# Function to elicit sql response from model
 def greet(prompt):
-    txt = f'''/*Prompt: {prompt}*/ \nSQL code:'''
+    txt= (f'''/*Prompt: {prompt}*/ \n —-SQL Code:\n''')
     sql = gpt3(txt)
     return sql
 
-e = gr.Interface(
-    fn=greet, 
-    inputs=["text"], 
-    outputs="text",
-    title="Natural Language to SQL Converter",
-    description="Convert your natural language queries into SQL code."
-)
-e.launch()
+
+#Code to set up Gradio UI
+iface = gr.Interface(greet, inputs = ["text"], outputs = "text",title="Natural Language to SQL", description="Enter any prompt and get a SQL statement back! For better results, give it more context")
+iface.launch()