Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from todset import todset
+from keras.
+
+def train(data: str, message: str):
+    if "→" not in data and "\n" not in data:
+        return "Dataset should be like:\nquestion→answer\nquestion→answer\netc."
+    dset, responses = todset(data)
+    tokenizer = Tokenizer()
+    tokenizer.fit_on_texts(list(dset.keys()))
+    
+    vocab_size = len(tokenizer.word_index) + 1
+    
+    model = Sequential()
+    model.add(Embedding(input_dim=vocab_size, output_dim=emb_size, input_length=inp_len))
+    model.add(SeqSelfAttention())
+    model.add(Flatten())
+    model.add(Dense(1024, activation="relu"))
+    model.add(Dropout(0.5))
+    model.add(Dense(512, activation="relu"))
+    model.add(Dense(512, activation="relu"))
+    model.add(Dense(256, activation="relu"))
+    model.add(Dense(dset_size, activation="softmax"))
+    
+    X = []
+    y = []
+    
+    for key in dset:
+        tokens = tokenizer.texts_to_sequences([key,])[0]
+        X.append(np.array((list(tokens)+[0,]*inp_len)[:inp_len]))
+        output_array = np.zeros(dset_size)
+        output_array[dset[key]] = 1
+        y.append(output_array)
+    
+    X = np.array(X)
+    y = np.array(y)
+    
+    model.compile(loss="categorical_crossentropy", metrics=["accuracy",])
+    
+    model.fit(X, y, epochs=10, batch_size=8, workers=4, use_multiprocessing=True)
+    tokens = tokenizer.texts_to_sequences([message,])[0]
+    prediction = model.predict(np.array((list(tokens)+[0,]*inp_len)[:inp_len]))
+    max_o = 0
+    max_v = 0
+    for ind, i in enumerate(prediction):
+        if max_v < i:
+            max_v = i
+            max_o = ind
+    return responses[ind]
+
+iface = gr.Interface(fn=greet, inputs=["text", "text"], outputs="text")
+iface.launch()