test

app.py

@@ -1,17 +1,40 @@
 import gradio as gr
 from transformers import pipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+# def predict(input_img):
+#     predictions = pipeline(input_img)
+#     return input_img, {p["label"]: p["score"] for p in predictions} 
+
+from typing import List
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("juierror/text-to-sql-with-table-schema")
+model = AutoModelForSeq2SeqLM.from_pretrained("juierror/text-to-sql-with-table-schema")
+
+def prepare_input(question: str, table: List[str]):
+    table_prefix = "table:"
+    question_prefix = "question:"
+    join_table = ",".join(table)
+    inputs = f"{question_prefix} {question} {table_prefix} {join_table}"
+    input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids
+    return input_ids
+
+def inference(question: str, table: List[str]) -> str:
+    input_data = prepare_input(question=question, table=table)
+    input_data = input_data.to(model.device)
+    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700)
+    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
+    return result
+
+# print(inference(question="get people name with age equal 25", table=["id", "name", "age"]))
 
 gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+    inference,
+    inputs="textbox",
+    outputs="label",
+    title="Text To SQL",
 )
 
 if __name__ == "__main__":

requirements.txt

@@ -1,2 +1,3 @@
 transformers
-torch
+torch
+typing