Update app.py

app.py

@@ -3,17 +3,16 @@ import logging
 
 import datasets
 import gradio as gr
-import sentence_transformers
 
 logging.disable(logging.CRITICAL)
 
-model = sentence_transformers.SentenceTransformer(
-    "dangvantuan/sentence-camembert-large", device="cpu")
+model_name = "dangvantuan/sentence-camembert-large"
+model = gradio.load(model_name)
 
-dataset = datasets.load_dataset("json", data_files=["./dataset.json"], split="train")
+dataset = datasets.load_dataset("json", data_files=["./data/dataset.json"], split="train")
 dataset.load_faiss_index("embeddings", "index.faiss")
 
-def search(query, k=3):
+def search(query, k):
     query_embedding = model.encode(query)
     _, retrieved_examples = dataset.get_nearest_examples(
         "embeddings",
@@ -39,36 +38,20 @@ def search(query, k=3):
     return results
 
 iface = gr.Interface(
-    fn=search,
+    search,
     inputs=[
-        gr.inputs.Textbox(
-            label="Query", placeholder="Type in a search query...", lines=3
-        ),
-        gr.inputs.Number(
-            label="K",
-            default=3,
-            description="Number of results to return",
-        ),
+        gr.inputs.Textbox(label="Query"),
+        gr.inputs.Number(label="K", default=3, min_value=1, max_value=10),
     ],
     outputs=[
-        gr.outputs.Label(
-            label="Result 1", type="auto", default="Search results will appear here."
-        ),
-        gr.outputs.Label(
-            label="Result 2", type="auto", default=""
-        ),
-        gr.outputs.Link(
-            label="Result 3", type="auto", default=""
-        ),
+        gr.outputs.Textbox(label="Result 1"),
+        gr.outputs.Textbox(label="Result 2"),
+        gr.outputs.Textbox(label="Result 3"),
     ],
     title="Camembert and Faiss-powered Search Engine",
     description="Search through a dataset using Camembert and Faiss",
-    theme="default",
+    theme="light",
     layout="vertical",
-    allow_flagging=False,
-    allow_screenshot=False,
-    allow_share=True,
-    allow_download=False
 )
 
 iface.launch()