Update app.py

app.py

@@ -4,7 +4,35 @@ import torch
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 """
-def predict(input, history=[]):
+
+import random
+import json
+
+import torch
+
+from model import NeuralNet
+from nltk_utils import bag_of_words, tokenize
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+with open('./intents.json', 'r') as json_data:
+    intents = json.load(json_data)
+
+FILE = "./data.pth"
+data = torch.load(FILE)
+
+input_size = data["input_size"]
+hidden_size = data["hidden_size"]
+output_size = data["output_size"]
+all_words = data['all_words']
+tags = data['tags']
+model_state = data["model_state"]
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+model.load_state_dict(model_state)
+model.eval()
+
+def predict(sentence, history=[]):
     """
     # tokenize the new input sentence
     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
@@ -19,7 +47,25 @@ def predict(input, history=[]):
     response = tokenizer.decode(history[0]).split("<|endoftext|>")
     response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
     """
-    return 'hi', 'hello'
+
+    sentence = tokenize(sentence)
+    X = bag_of_words(sentence, all_words)
+    X = X.reshape(1, X.shape[0])
+    X = torch.from_numpy(X).to(device)
+
+    output = model(X)
+    _, predicted = torch.max(output, dim=1)
+
+    tag = tags[predicted.item()]
+
+    probs = torch.softmax(output, dim=1)
+    prob = probs[0][predicted.item()]
+    if prob.item() > 0.75:
+        for intent in intents['intents']:
+            if tag == intent["tag"]:
+                reply = random.choice(intent['responses'])
+    
+    return reply, history
 
 import gradio as gr