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HusnaManakkot
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1418d64
Update app.py
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app.py
CHANGED
@@ -1,37 +1,45 @@
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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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# Load the Spider dataset
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spider_dataset = load_dataset("spider", 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
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#
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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
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# Create a Gradio interface
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interface = gr.Interface(
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fn=
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inputs=gr.
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outputs=[gr.Textbox(label="
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title="NL to SQL with T5 using Spider Dataset",
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description="This model
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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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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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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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# 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()
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