Add Application file

app.py

@@ -0,0 +1,81 @@
+from statistics import mean
+import random
+
+import torch
+from transformers import BertModel, BertTokenizerFast
+import numpy as np
+import torch.nn.functional as F
+import gradio as gr
+
+threshold = 0.4
+
+tokenizer = BertTokenizerFast.from_pretrained("setu4993/LaBSE")
+model = BertModel.from_pretrained("setu4993/LaBSE")
+model = model.eval()
+
+order_food_ex = [
+    "food",
+    "I am hungry, I want to order food",
+    "How do I order food",
+    "What are the food options",
+    "I need dinner",
+    "I want lunch",
+    "What are the menu options",
+    "I want a hamburger"
+        ]
+
+talk_to_human_ex = [
+    "I need to talk to someone",
+    "Connect me with a human",
+    "I need to speak with a person",
+    "Put me on with a human",
+    "Connect me with customer service",
+    "human"
+        ]
+
+def embed(text, tokenizer, model):
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return outputs.pooler_output
+
+def similarity(embeddings_1, embeddings_2):
+    normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
+    normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
+    return torch.matmul(
+        normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
+    )
+
+order_food_embed = [embed(x, tokenizer, model) for x in order_food_ex]
+talk_to_human_embed = [embed(x, tokenizer, model) for x in talk_to_human_ex]
+
+def chat(message, history):
+    history = history or []
+    message_embed = embed(message, tokenizer, model)
+    order_sim = []
+    for em in order_food_embed:
+        order_sim.append(float(similarity(em, message_embed)))
+    human_sim = []
+    for em in talk_to_human_embed:
+        human_sim.append(float(similarity(em, message_embed)))
+    if mean(order_sim) > threshold:
+        response = random.choice([
+            "We have hamburgers or pizza! Which one do you want?",
+            "Do you want a hamburger or a pizza?"])
+    elif mean(human_sim) > threshold:
+        response = random.choice([
+            "Sure, a customer service agent will jump into this convo shortly!", 
+            "No problem. Let me forward on this conversation to a person that can respond."])
+    else:
+        response = "Sorry, I didn't catch that. Could your rephrase?"
+    history.append((message, response))
+    return history, history
+
+iface = gr.Interface(
+    chat,
+    ["text", "state"],
+    ["chatbot", "state"],
+    allow_screenshot=False,
+    allow_flagging="never",
+)
+iface.launch()