add app.py and requirement.txt

app.py

@@ -0,0 +1,228 @@
+# Load the packages
+import torch
+import streamlit as st
+from transformers import GPT2Tokenizer, GPT2LMHeadModel,BartTokenizer,BartForConditionalGeneration
+import spacy
+import spacy.cli
+spacy.cli.download("en_core_web_sm")
+nlp=spacy.load("en_core_web_sm")
+nlp=spacy.load("en_core_web_sm")
+from spacy import displacy
+
+#---Sidebar Design-----
+
+st.sidebar.subheader("Select from the dropdown list") # add the subheader of sidebar
+st.sidebar.text("") # add line space
+
+option_lang = st.sidebar.selectbox(
+    'What is your native language?',
+    ('Japanese', 'Madarin'))   # add a dropdown list for native languages
+
+st.sidebar.write('You selected:', option_lang)  # display the selected native language
+
+st.sidebar.text("") # add line space
+
+
+option_model=st.sidebar.selectbox(
+    'Which language model would like to use?',
+    ('GPT-2', 'BART'))  # add a dropdown list for language model
+
+st.sidebar.write('You selected:', option_model)  # display the selected language model
+
+#---Main Body Design-----
+
+st.title('Make Friends with English 🤝')  # add a title for the web app
+
+st.text("") # add line space
+
+st.markdown('This web app is designed for ESL speakers who may face difficulty in communicating context in English.')
+st.text("") # add line space
+
+st.markdown('<p style="font-size:20px;"><strong>Enter your sentence 👇</strong></p>',unsafe_allow_html=True)  # add a subtitle
+
+original = st.text_input('', '',label_visibility="collapsed") # add a textbox to input original sentence
+
+go = st.button('Generate')   # add a 'Generate button' to run the selected language model
+
+# Define the output directory
+if option_model=='GPT-2':
+    output_dir = "7. Models/"+'80K_GPT2_v2'+"/"
+
+else:
+    output_dir = "7. Models/"+'80K_BART_v2'+"/"
+
+
+# Assign cuda to the device to use for training
+if torch.cuda.is_available(): 
+    dev = "cuda:0" 
+    print("This model will run on CUDA")
+# elif  torch.backends.mps.is_available(): 
+#     dev = "mps:0"
+#     print("This model will run on MPS")
+else:
+    dev = "cpu" 
+    print("This model will run on CPU")
+device = torch.device(dev) 
+
+
+# Define the function to generate corrected sentence using GPT-2 model
+def generate_prediction(prompt, max_length=100, temperature=1.0, top_p=1.0):
+    model = GPT2LMHeadModel.from_pretrained(output_dir).to(device)
+    tokenizer = GPT2Tokenizer.from_pretrained(output_dir)
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+    attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device)
+    with torch.no_grad():
+        output = model.generate(
+            input_ids, 
+            attention_mask=attention_mask, 
+            max_length=max_length, 
+            num_return_sequences=1, 
+            no_repeat_ngram_size=2,
+            temperature=temperature,
+            top_p=top_p,
+        )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Define the function to extract the output (corrected sentence)
+def model_running(model):
+    if go and model=='GPT-2':
+        try:
+            tokenizer = GPT2Tokenizer.from_pretrained(output_dir)
+            prompt = f"input: {original} output:"
+            prompt_length = len(tokenizer.encode(prompt))
+            dynamic_max_length = int(1.5 * len(original.split())) + prompt_length
+
+            # Generate prediction
+            prediction = generate_prediction(prompt, max_length=dynamic_max_length, temperature=0.8, top_p=0.8)
+
+            # Extract the actual generated output
+            generated_output = prediction.split("output:")[1].strip()
+
+            return generated_output
+        
+        except Exception as e:
+            st.exception("Exception: %s\n" % e)
+
+    elif go and model=='BART':
+        try:
+            model = BartForConditionalGeneration.from_pretrained(output_dir)
+            tokenizer = BartTokenizer.from_pretrained(output_dir)
+
+            # Tokenize the input text
+            input_ids = tokenizer.encode(original, return_tensors='pt')
+
+            # Generate text with the fine-tuned BART model
+            output_ids = model.generate(input_ids)
+
+            # Decode the output text
+            generated_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+            return generated_output
+
+        except Exception as e:
+            st.exception("Exception: %s\n" % e)
+
+
+output=model_running(option_model)
+
+# Add the warning message based on the output
+if output is None:
+    st.markdown('<span style="color: #FF4500;">Note: Please enter your sentence and click **Generate** button!</span>',unsafe_allow_html=True)
+else:
+    st.text("")
+
+st.markdown('<p style="font-size:20px;"><strong>Recommended sentence 💡</strong></p>',unsafe_allow_html=True) # add a subtitle
+
+st.text(output)  # display the corrected sentence
+
+st.text("") # add line space
+
+st.markdown('<p style="font-size:20px;"><strong>Part-of-speech Tagging 🏷</strong></p>',unsafe_allow_html=True)  # add a subtitle
+
+# Add the POS tags
+if original!='' and output is not None:
+    doc=nlp(output)
+    for token in doc:
+        st.write(token,token.pos_)
+
+st.text("") # add line space
+
+st.markdown('<p style="font-size:20px;"><strong>Dependency Tree 🌳</strong></p>',unsafe_allow_html=True)  # add a subtitle
+
+# Add a html wrapper to hold the html file of dependency tree
+HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
+
+# Add the dependency tree
+if original!='' and output is not None:
+    doc=nlp(output)
+    docs = [span.as_doc() for span in doc.sents]
+    html=displacy.render(docs,style='dep')
+    st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
+
+
+st.markdown('___')
+st.markdown('by [A very beta ChatGPT-4.5](https://github.com/danish-sven/anlp-at2-gpt45/)')   # add the author
+
+
+# # The code below is to generate corrected sentences with GPT-2 or BART model.
+# if go and option_model=='GPT-2':
+#     try:
+
+#         model = GPT2LMHeadModel.from_pretrained(output_dir).to(device)
+#         tokenizer = GPT2Tokenizer.from_pretrained(output_dir)
+
+#         def generate_prediction(prompt, max_length=100, temperature=1.0, top_p=1.0):
+#             input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+#             attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device)
+#             with torch.no_grad():
+#                 output = model.generate(
+#                     input_ids, 
+#                     attention_mask=attention_mask, 
+#                     max_length=max_length, 
+#                     num_return_sequences=1, 
+#                     no_repeat_ngram_size=2,
+#                     temperature=temperature,
+#                     top_p=top_p,
+#                 )
+#             return tokenizer.decode(output[0], skip_special_tokens=True)
+
+#         # Set max_length dynamically based on the length of the original text
+#         prompt = f"input: {original} output:"
+#         prompt_length = len(tokenizer.encode(prompt))
+#         dynamic_max_length = int(1.5 * len(original.split())) + prompt_length
+
+#         # Generate prediction
+#         prediction = generate_prediction(prompt, max_length=dynamic_max_length, temperature=0.8, top_p=0.8)
+
+#         # Extract the actual generated output
+#         generated_output = prediction.split("output:")[1].strip()
+
+#         st.text(generated_output)
+
+#     except Exception as e:
+#         st.exception("Exception: %s\n" % e)
+
+# elif go and option_model=='BART':
+#     try:
+
+#         model = BartForConditionalGeneration.from_pretrained(output_dir)
+#         tokenizer = BartTokenizer.from_pretrained(output_dir)
+
+
+#         # Tokenize the input text
+#         input_ids = tokenizer.encode(original, return_tensors='pt')
+
+#         # Generate text with the fine-tuned BART model
+#         output_ids = model.generate(input_ids)
+
+#         # Decode the output text
+#         generated_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+#         st.text(generated_output)
+
+#     except Exception as e:
+#         st.exception("Exception: %s\n" % e)   
+
+
+
+

requirements.txt

@@ -0,0 +1,7 @@
+streamlit==1.22.0
+transformers
+torch
+spacy
+pandas
+# nltk==3.8.1
+# re