Upload 5 files

app.py

@@ -8,8 +8,8 @@ query_processor = QueryProcessor(db)
 
 def respond(message, history):
     """Processes user queries, fetches results from the database, and returns responses."""
-    response = query_processor.process_query(message)
-    return response
+    response ,sql_query = query_processor.process_query(message)
+    return response ,sql_query
 
 # Gradio Chat UI
 demo = gr.ChatInterface(

nlp/sql_model.py

@@ -1,12 +1,12 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-# from transformers import AutoTokenizer, AutoModelForCausalLM
+# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 class SQLModel:
     def __init__(self, model_name="google/flan-t5-base"):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-        # self.tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
-        # self.model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it",)
+        # self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        # self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
+        self.model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it",)
 
     def generate_sql(self, natural_language_query):
         input_text = f"""You are a highly skilled SQL translator. Your task is to convert natural language descriptions of data queries into correct and optimized SQL statements.