Upload app.py

src/app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+import math
+import torch
+
+model_name = "KateMaiatskaia1836/t5-pretrained_v1"
+max_input_length = 512
+
+st.header("Generate candidate titles for articles")
+
+st_model_load = st.text('Loading title generator model...')
+
+@st.cache_data()
+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 = 512
+    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 + "__")