Update app.py

app.py

@@ -1,21 +1,32 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-# Initialize the translation pipeline with a pre-trained model
-translator = pipeline("translation_en_to_fr", model="t5-small")
+# Load the tokenizer and model
+tokenizer = T5Tokenizer.from_pretrained('t5-small')
+model = T5ForConditionalGeneration.from_pretrained('t5-small')
 
-def translate(text):
-    # Translate the input text from English to French
-    result = translator(text, max_length=512)
-    return result[0]['translation_text']
+def generate_sql(question):
+    # Format the question for the model if needed. For example:
+    # input_text = f"translate English to SQL: {question}"
+    input_text = f"{question}"  # Directly use the question if the model is fine-tuned for SQL generation
+    
+    # Tokenize the input text
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    
+    # Generate the output sequence
+    output_ids = model.generate(input_ids, max_length=512, num_beams=5)[0]
+    
+    # Decode the generated ids to get the SQL query
+    sql_query = tokenizer.decode(output_ids, skip_special_tokens=True)
+    return sql_query
 
 # Define the Gradio interface
 iface = gr.Interface(
-    fn=translate,
-    inputs=gr.Textbox(lines=2, placeholder="Enter English text here..."),
-    outputs=gr.Textbox(),  # Updated for Gradio version 4.0
-    title="English to French Translation",
-    description="This app uses the T5 model to translate English text to French. Type some text and press submit."
+    fn=generate_sql,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
+    outputs=gr.Textbox(),
+    title="Natural Language to SQL",
+    description="This app uses a Seq2Seq model to generate SQL queries from natural language questions."
 )
 
 # Launch the app