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app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+import math
+import torch
+
+model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
+# model_name = "afnanmmir/t5-base-axriv-to-abstract-3"
+max_input_length = 1024
+max_output_length = 256
+
+st.header("Generate summaries")
+
+st_model_load = st.text('Loading summary generator model...')
+
+# @st.cache(allow_output_mutation=True)
+@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 summary for', value=st.session_state.text, height=500)
+
+def generate_summary():
+    st.session_state.text = st_text_area
+
+    # tokenize text
+    inputs = ["summarize: " + st_text_area]
+    # print(inputs)
+    inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)
+    # print("Tokenized inputs: ")
+    # print(inputs)
+
+    # 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, max_length=max_output_length)
+    
+    outputs = model.generate(**inputs, do_sample=True, max_length=max_output_length, early_stopping=True, num_beams=8, length_penalty=2.0, no_repeat_ngram_size=2, min_length=64)
+    # print("outputs", outputs)
+    decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    # print("Decoded_outputs", decoded_outputs)
+    predicted_summaries = nltk.sent_tokenize(decoded_outputs.strip())
+    print("Predicted summaries", predicted_summaries)
+
+    # decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    # predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
+
+    st.session_state.summaries = predicted_summaries
+
+# generate title button
+st_generate_button = st.button('Generate summary', on_click=generate_summary)
+
+# title generation labels
+if 'summaries' not in st.session_state:
+    st.session_state.summaries = []
+
+if len(st.session_state.summaries) > 0:
+    print("In summaries if")
+    with st.container():
+        st.subheader("Generated summaries")
+        for summary in st.session_state.summaries:
+            st.markdown("__" + summary + "__")

requirements.txt

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+nltk
+torch
+transformers