Upload app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import os
+import query_index
+import datasets
+import sentence_transformers
+
+def query(text, k=5):
+    model = sentence_transformers.SentenceTransformer(
+    "dangvantuan/sentence-camembert-large", device="cpu")
+
+    dataset = datasets.load_dataset("json", data_files=["./data/dataset.json"], split="train")
+    dataset.load_faiss_index("embeddings", "index.faiss")
+
+    query_embedding = model.encode(text)
+    _, retrieved_examples = dataset.get_nearest_examples(
+        "embeddings",
+        query_embedding,
+        k=k,
+                    )
+
+
+    for text, start, end, title, url in zip(
+        retrieved_examples["text"],
+        retrieved_examples["start"],
+        retrieved_examples["end"],
+        retrieved_examples["title"],
+        retrieved_examples["url"],
+    ):
+        start = start
+        end = end
+        print(f"title: {title}")
+        print(f"transcript: [{str(start)+' ====> '+str(end)}] {text}")
+        print(f"link: {url}")
+        print("*" * 10)
+
+iface = gr.Interface(
+  fn=query, 
+  inputs='text',
+  outputs='text',
+  examples=[["Qu'est ce qui t'a fait le plus progresser?"]]
+)
+
+iface.launch()