Update app.py

app.py

@@ -1,51 +1,37 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-model_path = "defog/sqlcoder-7b-2"
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16, device_map="auto")
+# Load the GaussAlgo model
+model_path = "gaussalgo/T5-LM-Large-text2sql-spider"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 
 def generate_sql(payload):
+    # Extract parts from the JSON payload
     question = payload.get("question", "")
     schema = payload.get("schema", "")
     sample_rows = payload.get("sample_rows", [])
-    
-    sample_str = "\n".join([str(row) for row in sample_rows]) if sample_rows else ""
-
-    prompt = f"""
-### Task
-Generate a SQL query to answer [QUESTION]{question}[/QUESTION]
-
-### Database Schema
-The query will run on a database with the following schema:
-{schema}
-
-### Sample Rows
-{sample_str}
-
-### Answer
-Given the database schema, here is the SQL query that [QUESTION]{question}[/QUESTION]
-[SQL]
-    """.strip()
-
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(
-        **inputs,
-        max_length=512,
-        do_sample=False,
-        num_beams=4,
-        early_stopping=True
-    )
-    sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return sql.split("[SQL]")[-1].strip()
 
+    # Convert sample rows into a single string
+    sample_str = " ".join([str(row) for row in sample_rows]) if sample_rows else ""
+
+    # Build model input prompt
+    prompt = f"Question: {question} Schema: {schema} Sample Rows: {sample_str}"
+
+    # Tokenize and generate
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=512)
+    generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return generated_sql
+
+# Gradio interface
 demo = gr.Interface(
     fn=generate_sql,
     inputs=gr.JSON(label="Input JSON (question, schema, sample_rows)"),
     outputs="text",
-    title="SQLCoder - Text to SQL",
-    description="Enter a JSON object with 'question', 'schema', and optional 'sample_rows'. The model will generate SQL using Defog's sqlcoder-7b-2."
+    title="Text-to-SQL Generator",
+    description="Enter a JSON object with 'question', 'schema', and optional 'sample_rows'. The model will generate SQL."
 )
 
-demo.launch()
+demo.launch()