Create app.py

app.py

@@ -0,0 +1,47 @@
+
+import streamlit as st
+
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "NL2SQL_BLOOMZ-3B"
+HUGGING_FACE_USER_NAME = "abhishek23HF"
+
+peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+
+from IPython.display import display, Markdown
+
+def make_inference(db_id, question):
+  batch = tokenizer(f"""
+  ### INSTRUCTION\n
+  Below is a User Question for a SQL DATABASE. Your job is to write a SQL Query for the given question from the user for that particular Database.
+  \n\n
+  ### DATABASE_ID:\n{db_id}\n
+  ### USER QUESTION:\n{question}\n\n
+  ### SQL QUERY:\n
+  """, return_tensors='pt')
+
+  with torch.cuda.amp.autocast():
+      output_tokens = model.generate(**batch, max_new_tokens=200)
+
+  return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+
+
+# Create two text input boxes
+text_input_db_id= st.text_input("DB ID")
+text_input_question = st.text_input("User Query")
+
+# make_inference(your_db_id_here, your_db_query_here)
+
+# Display the text input boxes
+
+if st.button('Submit'):
+    st.write(make_inference(text_input_db_id, text_input_question))