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app.py

@@ -9,46 +9,45 @@ model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revi
 
 # Precompute reference embeddings
 ref_texts = [
-            "Theatro App: Hello John. Hey John. Hi John. Call John",
-                "Theatro App: Message John. Message for John. Leave a message for John",
-                    "Theatro App: Play messages. Listen to messages",
-                        "Theatro App: What time is it?",
-                            "Theatro App: What time is it?",
-                                "Theatro App: Cashier Backup. Backup Cashier. Register backup. Register assistance.",
-                                    "Theatro App: repeat",
-                                        "Theatro App: Check inventory",
-                                            "Theatro App: Check Sales",
-                                                "Theatro App: Curbside Pickup",
-                                                    "Theatro App: Replay last message.",
-                                                        "Theatro App: Post it. Post it for group"
-                                                            "Theatro App: Announcement. Announcement for the group",
-                                                                "Open question: This is about products sold in TractorSupply.",
-                                                                    "Open question: This is about pet care.",
-                                                                        "Open question: What is the weather like?",
-                                                                            "Open question: What's 15% off from $79.99?",
-                                                                                "Open question: Can you look up the skew for 1091784?",
-                                                                                ]
+    "Theatro App: Hello John. Hey John. Hi John. Call John",
+    "Theatro App: Message John. Message for John. Leave a message for John",
+    "Theatro App: Play messages. Listen to messages",
+    "Theatro App: What time is it?",
+    "Theatro App: What time is it?",
+    "Theatro App: Cashier Backup. Backup Cashier. Register backup. Register assistance.",
+    "Theatro App: repeat",
+    "Theatro App: Check inventory",
+    "Theatro App: Check Sales",
+    "Theatro App: Curbside Pickup",
+    "Theatro App: Replay last message.",
+    "Theatro App: Post it. Post it for group"
+    "Theatro App: Announcement. Announcement for the group",
+    "Open question: This is about products sold in TractorSupply.",
+    "Open question: This is about pet care.",
+    "Open question: What is the weather like?",
+    "Open question: What's 15% off from $79.99?",
+    "Open question: Can you look up the skew for 1091784?",
+]
 
 ref_embeddings = model.encode(ref_texts, convert_to_tensor=True)
 
 def find_query_type(query):
-        query_embeddings = model.encode([query], convert_to_tensor=True)
-            scores = F.cosine_similarity(query_embeddings, ref_embeddings, dim=-1)
-                max_index = torch.argmax(scores).item()
-                    ref_text = ref_texts[max_index]
-                        query_type = ref_text.split(": ")[0]
-                            return query_type
+    query_embeddings = model.encode([query], convert_to_tensor=True)
+    scores = F.cosine_similarity(query_embeddings, ref_embeddings, dim=-1)
+    max_index = torch.argmax(scores).item()
+    ref_text = ref_texts[max_index]
+    query_type = ref_text.split(": ")[0]
+    return query_type
 
-                        import gradio as gr
-                        def predict(query):
-                                query_type = find_query_type(query)
-                                    return query_type
-
-                                iface = gr.Interface(fn=predict,
-                                                             inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."),
-                                                                                  outputs="text",
-                                                                                                       title="Query Type Classifier",
-                                                                                                                            description="This model classifies the type of your query. Just input your query and get the predicted category.")
+import gradio as gr
+def predict(query):
+    query_type = find_query_type(query)
+    return query_type
 
-                                iface.launch()
+iface = gr.Interface(fn=predict,
+                     inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."),
+                     outputs="text",
+                     title="Query Type Classifier",
+                     description="This model classifies the type of your query. Just input your query and get the predicted category.")
 
+iface.launch()