Update app.py

app.py

@@ -6,17 +6,15 @@ from sklearn.feature_extraction.text import CountVectorizer
 from tensorflow.keras.models import Model
 from tensorflow.keras import models
 from tensorflow.keras.layers import Input,LSTM,Dense
-#translation
 input_texts_translation=['again.', 'arrive.', 'bathroom.', 'believe.', 'can.', 'deaf.', 'fine.', 'go.', 'hello.', 'help.', 'home.', 'how.', 'hungury.', 'sorry.', 'call.', 'later.', 'learn.', 'like.', 'live.', 'meet.', 'my.', 'name.', 'nice.', 'no.', 'please.', 'see.', 'share.', 'sign.', 'slow.', 'takecare.', 'talk.', 'thank you.', 'time.', 'understand.', 'we.', 'what.', 'when.', 'where.', 'who.', 'yes.', 'you.', 'you.', 'you.', 'you.', 'name.', 'good.', 'everning.', 'night.', 'how you.', 'you name what.', 'my name.', 'you live where.', 'i.', 'live.', 'you help.', 'yes help me.', 'you understand.', 'i hungry.', 'good everning.', 'good night.', 'pleased.', 'nice meet you.', 'i fine.', 'home arrive when.', 'where bathroom.', 'believe my.', 'call.', 'call.','deaf.', 'i call later.', 'i deaf.', 'what time.', 'i sorry.', 'on my.', 'my.', 'believe.', 'learn.', 'learn.', 'what are you learning.', 'you learn sign where.', 'i go home.', 'i.', 'i.', 'see you later.', 'meet.', 'meet.', 'we meet.', 'like.', 'i like.', 'talk later.', 'later.', 'later.']
 input_characters_translation=[' ', '.', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y']
-target_characters_translation=['\t', '\n', ' ', '.', 'ं', 'आ', 'उ', 'क', 'ख', 'ग', 'घ', 'च', 'ज', 'झ', 'ट', 'ड', 'ण', 'त', 'द', 'न', 'प', 'फ', 'ब', 'भ', 'म', 'य', 'र', 'ल', 'ळ', 'व', 'श', 'स', 'ह', 'ा', 'ि', 'ी', 'ु', 'ू', 'े', 'ो', '्']
-
+target_characters_translation=['\t', '\n', ' ', 'ं', 'आ', 'उ', 'क', 'ख', 'ग', 'घ', 'च', 'ज', 'झ', 'ट', 'ड', 'ण', 'त', 'द', 'न', 'प', 'फ', 'ब', 'भ', 'म', 'य', 'र', 'ल', 'ळ', 'व', 'श', 'स', 'ह', 'ा', 'ि', 'ी', 'ु', 'ू', 'े', 'ो', '्']
 
 num_en_chars_translation = 24
-num_dec_chars_translation = 41
+num_dec_chars_translation = 40
 
 max_input_length_translation = 22
-max_target_length_translation = 37
+max_target_length_translation = 36
 
 cv_translation=CountVectorizer(binary=True,tokenizer=lambda txt: txt.split(),stop_words=None,analyzer='char') 
 
@@ -26,8 +24,8 @@ enc_outputs_translation, state_h_enc_translation, state_c_enc_translation = mode
 
 en_model_translation = Model(model_translation.input[0], [state_h_enc_translation, state_c_enc_translation])
 
-dec_state_input_h_translation = Input(shape=(256,))
-dec_state_input_c_translation = Input(shape=(256,))
+dec_state_input_h_translation = Input(shape=(256,),name="input_3")
+dec_state_input_c_translation = Input(shape=(256,),name="input_4")
 dec_states_inputs_translation = [dec_state_input_h_translation, dec_state_input_c_translation]
 
 dec_lstm_translation = model_translation.layers[3]
@@ -41,39 +39,37 @@ dec_outputs_translation = dec_dense_translation(dec_outputs_translation)
 dec_model_translation = Model(
     [model_translation.input[1]] + dec_states_inputs_translation, [dec_outputs_translation] + dec_states_translation
 )
+
 def decode_sequence_translation(input_seq):
-        
+     
         reverse_target_char_index_translation = dict(enumerate(target_characters_translation))
         
         states_value_translation = en_model_translation.predict(input_seq)
 
-        
+      
         co_translation=cv_translation.fit(target_characters_translation) 
         target_seq_translation=np.array([co_translation.transform(list("\t")).toarray().tolist()],dtype="float32")
 
-        
         stop_condition = False
-        
+
         decoded_sentence_translation = ""
 
         while not stop_condition:
-            
+      
             output_chars_translation, h_translation, c_translation = dec_model_translation.predict([target_seq_translation] + states_value_translation)
 
-           
             char_index_translation = np.argmax(output_chars_translation[0, -1, :])
             text_char_translation = reverse_target_char_index_translation[char_index_translation]
             decoded_sentence_translation += text_char_translation
-              
+             
             if text_char_translation == "\n" or len(decoded_sentence_translation) > max_target_length_translation:
                 stop_condition = True
-            
+      
             target_seq_translation = np.zeros((1, 1, num_dec_chars_translation))
             target_seq_translation[0, 0, char_index_translation] = 1.0
             states_value_translation = [h_translation, c_translation]
-        
+     
         return decoded_sentence_translation
-
 def bagofcharacter_translation(input_t):
         cv_translation=CountVectorizer(binary=True,tokenizer=lambda txt:
         txt.split(),stop_words=None,analyzer='char') 
@@ -87,6 +83,7 @@ def bagofcharacter_translation(input_t):
             en_in_data[0].append(pad_en)
     
         return np.array(en_in_data,dtype="float32")
+
 #transliteration
 
 input_characters_transliteration=[' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u','v', 'w', 'x', 'y', 'z']
@@ -96,7 +93,6 @@ num_dec_chars_transliteration = 68
 max_input_length_transliteration = 21
 max_target_length_transliteration = 20
 cv_transliteration=CountVectorizer(binary=True,tokenizer=lambda txt: txt.split(),stop_words=None,analyzer='char') 
-
 model_transliteration = models.load_model("s2s_transliteration")
 
 enc_outputs_transliteration, state_h_enc_transliteration, state_c_enc_transliteration = model_transliteration.layers[2].output 
@@ -106,7 +102,6 @@ en_model_transliteration = Model(model_transliteration.input[0], [state_h_enc_tr
 dec_state_input_h_transliteration = Input(shape=(256,), name="input_6")
 dec_state_input_c_transliteration = Input(shape=(256,), name="input_7")
 dec_states_inputs_transliteration = [dec_state_input_h_transliteration, dec_state_input_c_transliteration]
-
 dec_lstm_transliteration = model_transliteration.layers[3]
 dec_outputs_transliteration, state_h_dec_transliteration, state_c_dec_transliteration = dec_lstm_transliteration(
     model_transliteration.input[1], initial_state=dec_states_inputs_transliteration
@@ -114,7 +109,6 @@ dec_outputs_transliteration, state_h_dec_transliteration, state_c_dec_transliter
 dec_states_transliteration = [state_h_dec_transliteration, state_c_dec_transliteration]
 dec_dense_transliteration = model_transliteration.layers[4]
 dec_outputs_transliteration = dec_dense_transliteration(dec_outputs_transliteration)
-
 dec_model_transliteration = Model(
     [model_transliteration.input[1]] + dec_states_inputs_transliteration, [dec_outputs_transliteration] + dec_states_transliteration
 )
@@ -162,6 +156,7 @@ def bagofcharacter_transliteration(input_t):
 def translate_to_Konkani(sent):  
     
     input_text = sent.split(',') 
+    
     output_texts=""
     for x in input_text:
         term=x+"."
@@ -183,5 +178,4 @@ def translate_to_Konkani(sent):
     return output_texts
 
 iface = gr.Interface(fn=translate_to_Konkani, inputs="text", outputs="text")
-iface.launch()
-#
+iface.launch()