adding app

app.py

@@ -0,0 +1,140 @@
+from transformers import T5ForConditionalGeneration, T5TokenizerFast
+from torch.utils.data import DataLoader
+import streamlit as st
+import torch
+import os
+
+
+# Let us define the main page
+st.markdown("Translation page 🔠")
+
+# Dropdown for the translation type
+translation_type = st.sidebar.selectbox("Translation Type", options=["French ➡️ Wolof", "Wolof ➡️ French"])
+
+# define a dictionary of versions
+models = {
+    "Version ✌️": {
+        "French ➡️ Wolof": {
+            "checkpoints": "wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_fw_v4",
+            "tokenizer": "wolof-translate/wolof_translate/tokenizers/t5_tokenizers/tokenizer_v4.json",
+            "max_len": None
+        }
+    },
+    "Version ☝️": {
+        "French ➡️ Wolof": {
+            "checkpoints": "wolof-translate/wolof_translate/checkpoints/t5_small_custom_train_results_fw_v3",
+            "tokenizer": "wolof-translate/wolof_translate/tokenizers/t5_tokenizers/tokenizer_v3.json",
+            "max_len": 51
+            }
+    }
+}
+
+# Dropdown for the model version
+version = st.sidebar.selectbox("Model version", options=["Version ☝️", "Version ✌️"])
+
+# Recuperate the number of sentences to provide
+number = st.sidebar.number_input("Give the number of sentences that you want to provide", min_value = 1,
+          max_value = 100)
+
+# Recuperate the number of sentences to provide
+temperature = st.sidebar.slider("How randomly need you the translated sentences to be from 0% to 100%", min_value = 0,
+          max_value = 100)
+
+
+# make the process
+try:
+    # recuperate checkpoints
+    checkpoints = torch.load(os.path.join(models[version][translation_type]['checkpoints'], "best_checkpoints.pth"))
+
+    # recuperate the tokenizer
+    tokenizer_file = models[version][translation_type]['tokenizer']
+    
+    # recuperate the max length
+    max_len = models[version][translation_type]['max_len']
+    
+    # let us get the best model
+    @st.cache_resource
+    def get_model():
+        
+        # initialize the tokenizer
+        tokenizer = T5TokenizerFast(tokenizer_file=tokenizer_file)
+        
+        # initialize the model
+        model_name = 't5-small'
+        
+        model = T5ForConditionalGeneration.from_pretrained(model_name)
+        
+        # resize the token embeddings
+        model.resize_token_embeddings(len(tokenizer))
+        
+        model.load_state_dict(checkpoints['model_state_dict'])
+        
+        
+        return model, tokenizer
+
+    model, tokenizer = get_model()
+
+    # set the model to eval mode
+    _ = model.eval()
+
+    # Add a title
+    st.header("Translate French sentences onto Wolof 👌")
+
+
+    # Recuperate two columns
+    left, right = st.columns(2)
+
+    # recuperate sentences
+    left.subheader('Give me some sentences in French: ')
+
+    for i in range(number):
+        
+        left.text_input(f"- Sentence number {i + 1}", key = f"sentence{i}")
+
+    # run model inference on all test data
+    original_translations, predicted_translations, original_texts, scores = [], [], [], {}
+
+    # print a sentence recuperated from the session
+    right.subheader("Translation to Wolof:")
+
+    for i in range(number):
+        
+        sentence = st.session_state[f"sentence{i}"] + tokenizer.eos_token
+        
+        if not sentence == "":
+        
+            # Let us encode the sentences
+            encoding = tokenizer([sentence], return_tensors='pt', max_length=max_len, padding='max_length', truncation=True)
+            
+            # Let us recuperate the input ids
+            input_ids = encoding.input_ids
+            
+            # Let us recuperate the mask
+            mask = encoding.attention_mask
+            
+            # Let us recuperate the pad token id
+            pad_token_id = tokenizer.pad_token_id
+            
+            # perform prediction
+            predictions = model.generate(input_ids, do_sample = False, top_k = 50, max_length = max_len, top_p = 0.90,
+                                            temperature = temperature/100, num_return_sequences = 0, attention_mask = mask, pad_token_id = pad_token_id)
+            
+            # decode the predictions
+            predicted_sentence = tokenizer.batch_decode(predictions, skip_special_tokens = True)
+            
+            # provide the prediction
+            right.write(f"{i+1}. {predicted_sentence[0]}")
+            
+        else:
+            
+            # provide the prediction
+            right.write(f"{i+1}. ")
+    
+except Exception as e:
+    
+    st.warning("The chosen model is not available yet !", icon = "⚠️")
+    
+    # st.write(e)
+    
+
+