Update app.py

app.py

@@ -1,107 +1,3 @@
-# import streamlit as st
-# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-# import nltk
-# import math
-# import torch
-
-# # model_name = "fabiochiu/t5-base-medium-title-generation"
-# model_name = "momoyukki/t5-base-news-title-generation_model"
-# max_input_length = 512
-
-# st.header("Generate candidate titles for news")
-
-# st_model_load = st.text('Loading title generator model...')
-
-# @st.cache(allow_output_mutation=True)
-# def load_model():
-#     print("Loading model...")
-#     tokenizer = AutoTokenizer.from_pretrained(model_name)
-#     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-#     nltk.download('punkt')
-#     print("Model loaded!")
-#     return tokenizer, model
-
-# tokenizer, model = load_model()
-# st.success('Model loaded!')
-# st_model_load.text("")
-
-# with st.sidebar:
-#     st.header("Model parameters")
-#     if 'num_titles' not in st.session_state:
-#         st.session_state.num_titles = 5
-#     def on_change_num_titles():
-#         st.session_state.num_titles = num_titles
-#     num_titles = st.slider("Number of titles to generate", min_value=1, max_value=10, value=1, step=1, on_change=on_change_num_titles)
-#     if 'temperature' not in st.session_state:
-#         st.session_state.temperature = 0.7
-#     def on_change_temperatures():
-#         st.session_state.temperature = temperature
-#     temperature = st.slider("Temperature", min_value=0.1, max_value=1.5, value=0.6, step=0.05, on_change=on_change_temperatures)
-#     st.markdown("_High temperature means that results are more random_")
-
-# if 'text' not in st.session_state:
-#     st.session_state.text = ""
-# st_text_area = st.text_area('Text to generate the title for', value=st.session_state.text, height=500)
-
-# def generate_title():
-#     st.session_state.text = st_text_area
-
-#     # tokenize text
-#     inputs = ["summarize: " + st_text_area]
-#     inputs = tokenizer(inputs, return_tensors="pt")
-
-#     # compute span boundaries
-#     num_tokens = len(inputs["input_ids"][0])
-#     print(f"Input has {num_tokens} tokens")
-#     max_input_length = 500
-#     num_spans = math.ceil(num_tokens / max_input_length)
-#     print(f"Input has {num_spans} spans")
-#     overlap = math.ceil((num_spans * max_input_length - num_tokens) / max(num_spans - 1, 1))
-#     spans_boundaries = []
-#     start = 0
-#     for i in range(num_spans):
-#         spans_boundaries.append([start + max_input_length * i, start + max_input_length * (i + 1)])
-#         start -= overlap
-#     print(f"Span boundaries are {spans_boundaries}")
-#     spans_boundaries_selected = []
-#     j = 0
-#     for _ in range(num_titles):
-#         spans_boundaries_selected.append(spans_boundaries[j])
-#         j += 1
-#         if j == len(spans_boundaries):
-#             j = 0
-#     print(f"Selected span boundaries are {spans_boundaries_selected}")
-
-#     # transform input with spans
-#     tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
-#     tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
-
-#     inputs = {
-#         "input_ids": torch.stack(tensor_ids),
-#         "attention_mask": torch.stack(tensor_masks)
-#     }
-
-#     # compute predictions
-#     outputs = model.generate(**inputs, do_sample=True, temperature=temperature)
-#     decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-#     predicted_titles = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
-
-#     st.session_state.titles = predicted_titles
-
-# # generate title button
-# st_generate_button = st.button('Generate title', on_click=generate_title)
-
-# # title generation labels
-# if 'titles' not in st.session_state:
-#     st.session_state.titles = []
-
-# if len(st.session_state.titles) > 0:
-#     with st.container():
-#         st.subheader("Generated titles")
-#         for title in st.session_state.titles:
-#             st.markdown("__" + title + "__")
-
-
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import nltk
@@ -151,11 +47,43 @@ def generate_title():
     st.session_state.text = st_text_area
 
     # tokenize text
-    inputs = tokenizer.encode("summarize: " + st_text_area, return_tensors="pt", max_length=max_input_length, truncation=True)
+    inputs = ["summarize: " + st_text_area]
+    inputs = tokenizer(inputs, return_tensors="pt")
+
+    # compute span boundaries
+    num_tokens = len(inputs["input_ids"][0])
+    print(f"Input has {num_tokens} tokens")
+    max_input_length = 500
+    num_spans = math.ceil(num_tokens / max_input_length)
+    print(f"Input has {num_spans} spans")
+    overlap = math.ceil((num_spans * max_input_length - num_tokens) / max(num_spans - 1, 1))
+    spans_boundaries = []
+    start = 0
+    for i in range(num_spans):
+        spans_boundaries.append([start + max_input_length * i, start + max_input_length * (i + 1)])
+        start -= overlap
+    print(f"Span boundaries are {spans_boundaries}")
+    spans_boundaries_selected = []
+    j = 0
+    for _ in range(num_titles):
+        spans_boundaries_selected.append(spans_boundaries[j])
+        j += 1
+        if j == len(spans_boundaries):
+            j = 0
+    print(f"Selected span boundaries are {spans_boundaries_selected}")
+
+    # transform input with spans
+    tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
+    tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
+
+    inputs = {
+        "input_ids": torch.stack(tensor_ids),
+        "attention_mask": torch.stack(tensor_masks)
+    }
 
     # compute predictions
-    outputs = model.generate(inputs, num_return_sequences=num_titles, temperature=temperature, max_length=150)
-    decoded_outputs = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+    outputs = model.generate(**inputs, do_sample=True, temperature=temperature)
+    decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
     predicted_titles = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
 
     st.session_state.titles = predicted_titles
@@ -172,3 +100,4 @@ if len(st.session_state.titles) > 0:
         st.subheader("Generated titles")
         for title in st.session_state.titles:
             st.markdown("__" + title + "__")
+