HusnaManakkot commited on
Commit
1eb39b3
1 Parent(s): d40fcf6

Update app.py

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Files changed (1) hide show
  1. app.py +9 -19
app.py CHANGED
@@ -1,42 +1,32 @@
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  import gradio as gr
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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  from datasets import load_dataset
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- from difflib import get_close_matches
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- # Load the Spider dataset
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- spider_dataset = load_dataset("spider", split='train[:100]') # Increase the number of examples for better matching
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  # Load tokenizer and model
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  tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
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  model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
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- def find_closest_match(query, dataset):
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- questions = [item['question'] for item in dataset]
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- matches = get_close_matches(query, questions, n=1)
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- return matches[0] if matches else None
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-
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  def generate_sql_from_user_input(query):
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- # Find the closest match in the dataset
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- matched_query = find_closest_match(query, spider_dataset)
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- if not matched_query:
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- return "No close match found in the dataset.", ""
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-
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- # Generate SQL for the matched query
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- input_text = "translate English to SQL: " + matched_query
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  inputs = tokenizer(input_text, return_tensors="pt", padding=True)
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  outputs = model.generate(**inputs, max_length=512)
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  sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
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- return matched_query, sql_query
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  # Create a Gradio interface
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  interface = gr.Interface(
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  fn=generate_sql_from_user_input,
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  inputs=gr.Textbox(label="Enter your natural language query"),
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- outputs=[gr.Textbox(label="Matched Query from Dataset"), gr.Textbox(label="Generated SQL Query")],
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- title="NL to SQL with T5 using Spider Dataset",
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- description="This model finds the closest match in the Spider dataset for your query and generates the corresponding SQL."
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  )
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  # Launch the app
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  if __name__ == "__main__":
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  interface.launch()
 
 
1
  import gradio as gr
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  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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  from datasets import load_dataset
 
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+ # Load the WikiSQL dataset (optional, not used directly in SQL generation)
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+ wikisql_dataset = load_dataset("wikisql", split='train')
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  # Load tokenizer and model
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  tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
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  model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
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  def generate_sql_from_user_input(query):
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+ # Generate SQL for the user's query
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+ input_text = "translate English to SQL: " + query
 
 
 
 
 
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  inputs = tokenizer(input_text, return_tensors="pt", padding=True)
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  outputs = model.generate(**inputs, max_length=512)
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  sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ return query, sql_query
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  # Create a Gradio interface
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  interface = gr.Interface(
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  fn=generate_sql_from_user_input,
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  inputs=gr.Textbox(label="Enter your natural language query"),
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+ outputs=[gr.Textbox(label="Your Query"), gr.Textbox(label="Generated SQL Query")],
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+ title="NL to SQL with T5 using WikiSQL Dataset",
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+ description="This model generates an SQL query for your natural language input."
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  )
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  # Launch the app
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  if __name__ == "__main__":
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  interface.launch()
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+