Update app.py

app.py

@@ -1,15 +1,103 @@
+# 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)
+    
+#     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 + "__")
+
+
+# -------------------------------
+
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import nltk
 import math
 import torch
 
-model_name = "fabiochiu/t5-base-medium-title-generation"
-max_input_length = 512
+model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
+max_input_length = 1024
+max_output_length = 256
+min_output_length = 64
 
-st.header("Generate candidate titles for articles")
+st.header("Generate summaries for articles")
 
-st_model_load = st.text('Loading title generator model...')
+st_model_load = st.text('Loading summary generator model...')
 
 @st.cache(allow_output_mutation=True)
 def load_model():
@@ -24,78 +112,33 @@ 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)
+st_text_area = st.text_area('Text to generate the summary for', value=st.session_state.text, height=500)
 
-def generate_title():
+def generate_summary():
     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)
-    }
+    inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)
 
     # compute predictions
-    outputs = model.generate(**inputs, do_sample=True, temperature=temperature)
+    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=min_output_length)
     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]
+    predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
 
-    st.session_state.titles = predicted_titles
+    st.session_state.summaries = predicted_summaries
 
-# generate title button
-st_generate_button = st.button('Generate title', on_click=generate_title)
+# generate summary button
+st_generate_button = st.button('Generate summary', on_click=generate_summary)
 
-# title generation labels
-if 'titles' not in st.session_state:
-    st.session_state.titles = []
+# summary generation labels
+if 'summaries' not in st.session_state:
+    st.session_state.summaries = []
 
-if len(st.session_state.titles) > 0:
+if len(st.session_state.summaries) > 0:
     with st.container():
-        st.subheader("Generated titles")
-        for title in st.session_state.titles:
-            st.markdown("__" + title + "__")
+        st.subheader("Generated summaries")
+        for summary in st.session_state.summaries:
+            st.markdown("__" + summary + "__")