Update app.py

app.py

@@ -1,40 +1,41 @@
 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]')  # Load a subset of the dataset
+# Load the WikiSQL dataset (only the table schemas are needed for validation)
+wikisql_dataset = load_dataset("wikisql", split='train')
 
 # 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 validate_sql_against_schema(sql_query, schema):
+    # This is a placeholder function. You need to implement the logic to validate
+    # the SQL query against the table schema. The validation can be as simple or as
+    # complex as you need, depending on the requirements.
+    return True  # Assume the query is valid for now
 
 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 user's query
+    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)
 
-    # Find the corresponding SQL query in the dataset
+    # Validate the generated SQL query against the schemas in the dataset
     for item in wikisql_dataset:
-        if item['question'] == matched_query:
-            return matched_query, item['sql']['human_readable']
+        if validate_sql_against_schema(sql_query, item['sql']):
+            return query, sql_query
 
-    return "Match found, but corresponding SQL query not found in dataset.", ""
+    return query, "Generated SQL query is not consistent with the dataset."
 
 # 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="Corresponding SQL Query from Dataset")],
+    outputs=[gr.Textbox(label="Your Query"), 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 returns the corresponding SQL query from the dataset."
+    description="This model generates an SQL query for your natural language input and validates it against the WikiSQL dataset."
 )
 
 # Launch the app