Update app.py

app.py

@@ -1,11 +1,10 @@
-
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from datasets import load_dataset
 from difflib import get_close_matches
 
-# Load the Spider dataset
-spider_dataset = load_dataset("wikisql", split='train[:100]')  # Increase the number of examples for better matching
+# Load the WikiSQL dataset
+wikisql_dataset = load_dataset("wikisql", split='train[:100]')  # Increase the number of examples for better matching
 
 # Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
@@ -18,7 +17,7 @@ def find_closest_match(query, dataset):
 
 def generate_sql_from_user_input(query):
     # Find the closest match in the dataset
-    matched_query = find_closest_match(query, spider_dataset)
+    matched_query = find_closest_match(query, wikisql_dataset)
     if not matched_query:
         return "No close match found in the dataset.", ""
 
@@ -34,12 +33,52 @@ interface = gr.Interface(
     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."
+    title="NL to SQL with T5 using WikiSQL Dataset",
+    description="This model finds the closest match in the WikiSQL dataset for your query and generates the corresponding SQL."
 )
 
 # Launch the app
 if __name__ == "__main__":
     interface.launch()
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from datasets import load_dataset
+from difflib import get_close_matches
+
+# Load the WikiSQL dataset
+wikisql_dataset = load_dataset("wikisql", split='train[:100]')  # Increase the number of examples for better matching
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
+
+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_from_user_input(query):
+    # Find the closest match in the dataset
+    matched_query = find_closest_match(query, wikisql_dataset)
+    if not matched_query:
+        return "No close match found in the 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_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 WikiSQL Dataset",
+    description="This model finds the closest match in the WikiSQL dataset for your query and generates the corresponding SQL."
+)
 
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()