Update app.py

app.py

@@ -1,7 +1,26 @@
 import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-def query_reformulation(input):
-    return "Hello " + name + "!!"
+# Load the tokenizer and model from Hugging Face
+model_name = 'alexdong/query-reformulation-knowledge-base-t5-small'
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-demo = gr.Interface(fn=query_reformulation, inputs="text", outputs="text")
-demo.launch()
+# Define the function that will be run for every input
+def generate_text(input_text):
+    input_ids = tokenizer(f"reformulate: {input_text}", return_tensors="pt").input_ids
+    output_ids = model.generate(input_ids, max_length=50)
+    decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return decoded_output
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,         
+    inputs="text",            
+    outputs="text",           
+    title="Query Reformulation",
+    description="Enter a search query to see how the model rewrites it into RAG friendly subqueries.", # Description
+)
+
+# Display the interface
+iface.launch()