Update app.py

app.py

@@ -1,40 +1,35 @@
 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("spider", split='train[:100]')  # Increase the number of examples for better matching
+spider_dataset = load_dataset("spider", split='train[:1000]')
 
 # 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_dataset(index):
+    # Ensure the index is within the range of the dataset
+    index = int(index)  # Convert to integer in case it's passed as a string
+    if index < 0 or index >= len(spider_dataset):
+        return "Invalid index. Please enter a number between 0 and {}.".format(len(spider_dataset) - 1), ""
 
-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.", ""
-
-    # Generate SQL for the matched query
-    input_text = "translate English to SQL: " + matched_query
+    # Get the natural language query from the dataset
+    query = spider_dataset[index]['question']
+    input_text = "translate English to SQL: " + 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
+    return 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")],
+    fn=generate_sql_from_dataset,
+    inputs=gr.Number(label="Dataset Index (0-4)"),
+    outputs=[gr.Textbox(label="Natural Language Query"), 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."
+    description="This model converts natural language queries from the Spider dataset into SQL. Enter the index of the dataset entry (0-4)!"
 )
 
 # Launch the app