added chat interface

app.py

@@ -310,7 +310,8 @@ norm_model.to(device)
 
 models_dict = {'AttentionSeq2Seq-188M': attn_model, 'NormalSeq2Seq-188M': norm_model}
 
-def generate(models_str, sentence, max_len=12, word2idx=word2idx, idx2word=idx2word,
+def generateAttn(sentence, history, max_len=12,
+             word2idx=word2idx, idx2word=idx2word,
              device=device, tokenize=tokenize, preprocess_text=preprocess_text,
              lookup_words=lookup_words, models_dict=models_dict):
     """
@@ -322,7 +323,8 @@ def generate(models_str, sentence, max_len=12, word2idx=word2idx, idx2word=idx2w
     :param idx2word: index to word mapping
     :return: response
     """
-    model = models_dict[models_str]
+    history = history
+    model = models_dict['AttentionSeq2Seq-188M']
     model.eval()
     sentence = preprocess_text(sentence)
     tokens = tokenize(sentence)
@@ -341,13 +343,46 @@ def generate(models_str, sentence, max_len=12, word2idx=word2idx, idx2word=idx2w
     response = lookup_words(idx2word, outputs)
     return ' '.join(response).replace('<bos>', '').replace('<eos>', '').strip()
 
+def generateNorm(sentence, history, max_len=12,
+             word2idx=word2idx, idx2word=idx2word,
+             device=device, tokenize=tokenize, preprocess_text=preprocess_text,
+             lookup_words=lookup_words, models_dict=models_dict):
+    """
+    Generate response
+    :param model: model
+    :param sentence: sentence
+    :param max_len: maximum length of sequence
+    :param word2idx: word to index mapping
+    :param idx2word: index to word mapping
+    :return: response
+    """
+    history = history
+    model = models_dict['NormalSeq2Seq-188M']
+    model.eval()
+    sentence = preprocess_text(sentence)
+    tokens = tokenize(sentence)
+    tokens = [word2idx[token] if token in word2idx else word2idx['<unk>'] for token in tokens]
+    tokens = [word2idx['<bos>']] + tokens + [word2idx['<eos>']]
+    tokens = torch.tensor(tokens, dtype=torch.long).unsqueeze(1).to(device)
+    outputs = [word2idx['<bos>']]
+    with torch.no_grad():
+        encoder_outputs, hidden = model.encoder(tokens)
+    for t in range(max_len):
+        output, hidden = model.decoder(torch.tensor([outputs[-1]], dtype=torch.long).to(device), hidden, encoder_outputs)
+        top1 = output.max(1)[1]
+        outputs.append(top1.item())
+        if top1.item() == word2idx['<eos>']:
+            break
+    response = lookup_words(idx2word, outputs)
+    return ' '.join(response).replace('<bos>', '').replace('<eos>', '').strip()
 
+# demo = gr.ChatInterface(generate, title="AttentionSeq2Seq-188M")
 
-demo = gr.Interface(fn=generate,
-                    inputs=[gr.Radio(list(models_dict.keys()), label="Model"),
-                     gr.Textbox(lines=2, label="Input Text")],
-                     outputs=gr.Textbox(label="Output Text"))
-
+with gr.Blocks() as demo:
+    gr.ChatInterface(generateNorm,
+                     title="NormalSeq2Seq-188M")
+    gr.ChatInterface(generateAttn,
+                     title="AttentionSeq2Seq-188M")
 
 if __name__ == "__main__":
     demo.launch()