Upload app.py

app.py

@@ -0,0 +1,40 @@
+
+import gradio as gr
+from transformers import pipeline
+
+# Load the pre-trained model from Hugging Face
+
+import torch
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer, pipeline
+
+peft_model_id = "jinhybr/code-llama-7b-text-to-sql"
+# peft_model_id = args.output_dir
+
+# Load Model with PEFT adapter
+model = AutoPeftModelForCausalLM.from_pretrained(
+  peft_model_id,
+  device_map="auto",
+  torch_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
+# load into pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+
+# Define the Gradio interface
+def translate_to_sql(question):
+    strA = 'You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.\nSCHEMA:\nCREATE TABLE table_17429402_7 (school VARCHAR, last_occ_championship VARCHAR)'
+    combined_json_data = [{'content': strA, 'role': 'system'}, {'content': question, 'role': 'user'}]
+    prompt = pipe.tokenizer.apply_chat_template(combined_json_data, tokenize=False, add_generation_prompt=True)
+    outputs = pipe(prompt, max_new_tokens=256, do_sample=False, temperature=0.1, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
+    return outputs[0]['generated_text'][len(prompt):].strip()
+
+question_input = gr.inputs.Textbox(lines=7, label="Enter your question")
+output_text = gr.outputs.Textbox(label="Generated SQL Query")
+
+# Create the Gradio interface
+gr.Interface(fn=translate_to_sql, inputs=question_input, outputs=output_text, title="Text to SQL Translator", description="Translate English questions to SQL queries.").launch()
+
+# Create the Gradio interface
+gr.Interface(fn=classify_text, inputs=inputs, outputs=outputs, title="Sentiment Analysis", description="Predict the sentiment of text.").launch()