Update app.py

app.py

@@ -1,35 +1,40 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from datasets import load_dataset
+from difflib import get_close_matches
 
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
-model = AutoModelForSeq2SeqLM.from_pretrained("hrshtsharma2012/NL2SQL-Picard-final")
+# Load the Spider dataset
+spider_dataset = load_dataset("spider", split='train[:100]')  # Increase the number of examples for better matching
 
-# Initialize the pipeline
-nl2sql_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
 
-# Load a part of the WikiSQL dataset
-wikisql_dataset = load_dataset("wikisql", split='train[:5]')
+def find_closest_match(query, dataset):
+    questions = [item['question'] for item in dataset]
+    matches = get_close_matches(query, questions, n=1)
+    return matches[0] if matches else None
 
-def generate_sql(query):
-    results = nl2sql_pipeline(query)
-    sql_query = results[0]['generated_text']
-    # Post-process the output to ensure it's a valid SQL query
-    sql_query = sql_query.replace('<pad>', '').replace('</s>', '').strip()
-    return sql_query
+def generate_sql_from_user_input(query):
+    # Find the closest match in the dataset
+    matched_query = find_closest_match(query, spider_dataset)
+    if not matched_query:
+        return "No close match found in the dataset.", ""
 
-# Use examples from the WikiSQL dataset
-example_questions = [(question['question'],) for question in wikisql_dataset]
+    # Generate SQL for the matched query
+    input_text = "translate English to SQL: " + matched_query
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
+    outputs = model.generate(**inputs, max_length=512)
+    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return matched_query, sql_query
 
 # Create a Gradio interface
 interface = gr.Interface(
-    fn=generate_sql,
-    inputs=gr.Textbox(lines=2, placeholder="Enter your natural language query here..."),
-    outputs="text",
-    examples=example_questions,
-    title="NL to SQL with Picard",
-    description="This model converts natural language queries into SQL using the WikiSQL dataset. Try one of the example questions or enter your own!"
+    fn=generate_sql_from_user_input,
+    inputs=gr.Textbox(label="Enter your natural language query"),
+    outputs=[gr.Textbox(label="Matched Query from Dataset"), gr.Textbox(label="Generated SQL Query")],
+    title="NL to SQL with T5 using Spider Dataset",
+    description="This model finds the closest match in the Spider dataset for your query and generates the corresponding SQL."
 )
 
 # Launch the app