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import streamlit as st
import pandas as pd
from transformers import pipeline
from stqdm import stqdm
from simplet5 import SimpleT5
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import BertTokenizer
from tensorflow.keras.models import load_model
from tensorflow.nn import softmax
import numpy as np
from datetime import datetime
import logging
import pip
date = datetime.now().strftime(r"%Y-%m-%d")
model_classes ={
0: "Ads",
1: "Apps",
2: "Battery",
3: "Charging",
4: "Delivery",
5: "Display",
6: "FOS",
7: "HW",
8: "Order",
9: "Refurb",
10: "SD",
11: "Setup",
12: "Unknown",
13: "WiFi",
}
@st.cache(allow_output_mutation=True,suppress_st_warning=True)
def load_t5():
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
tokenizer = AutoTokenizer.from_pretrained("t5-base")
return model, tokenizer
@st.cache(allow_output_mutation=True,suppress_st_warning=True)
def custom_model():
return pipeline("summarization", model="my_awesome_sum/")
@st.cache(allow_output_mutation=True,suppress_st_warning=True)
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv(index=False).encode("utf-8")
@st.cache(allow_output_mutation=True,suppress_st_warning=True)
def load_one_line_summarizer(model):
return model.load_model("t5", "snrspeaks/t5-one-line-summary")
@st.cache(allow_output_mutation=True,suppress_st_warning=True)
def classify_category():
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
new_model = load_model("model")
return tokenizer, new_model
st.set_page_config(layout="wide", page_title="Amazon Review Summarizer")
st.title("Amazon Review Summarizer")
uploaded_file = st.file_uploader("Choose a file", type=["xlsx", "xls", "csv"])
summarizer_option = st.selectbox(
"Select Summarizer",
("Custom trained on the dataset", "t5-base", "t5-one-line-summary"),
)
classification = st.checkbox("Classify Category", value=True)
ps = st.empty()
if st.button("Process",type="primary"):
cancel_button=st.empty()
cancel_button2=st.empty()
cancel_button3=st.empty()
if uploaded_file is not None:
if uploaded_file.name.split(".")[-1] in ["xls", "xlsx"]:
df = pd.read_excel(uploaded_file, engine="openpyxl")
if uploaded_file.name.split(".")[-1] in [".csv"]:
df = pd.read_csv(uploaded_file)
columns = df.columns.values.tolist()
columns = [x.lower() for x in columns]
df.columns = columns
print(summarizer_option)
try:
text = df["text"].values.tolist()
if summarizer_option == "Custom trained on the dataset":
model = custom_model()
progress_text = "Summarization in progress. Please wait."
summary = []
for x in stqdm(range(len(text))):
if cancel_button.button("Cancel",key=x):
del model
break
try:
summary.append(
model(
f"summarize: {text[x]}",
max_length=50,
early_stopping=True,
)[0]["summary_text"]
)
except:
pass
output = pd.DataFrame(
{"text": df["text"].values.tolist(), "summary": summary}
)
if classification:
classification_token, classification_model = classify_category()
tf_batch = classification_token(
text,
max_length=128,
padding=True,
truncation=True,
return_tensors="tf",
)
with st.spinner(text="identifying theme"):
tf_outputs = classification_model(tf_batch)
classes = []
with st.spinner(text="creating output file"):
for x in stqdm(range(len(text))):
tf_o = softmax(tf_outputs["logits"][x], axis=-1)
label = np.argmax(tf_o, axis=0)
keys = model_classes
classes.append(keys.get(label))
output["category"] = classes
csv = convert_df(output)
st.download_button(
label="Download data as CSV",
data=csv,
file_name=f"{summarizer_option}_{date}_df.csv",
mime="text/csv",
)
if summarizer_option == "t5-base":
model, tokenizer = load_t5()
summary = []
for x in stqdm(range(len(text))):
if cancel_button2.button("Cancel",key=x):
del model,tokenizer
break
tokens_input = tokenizer.encode(
"summarize: " + text[x],
return_tensors="pt",
max_length=tokenizer.model_max_length,
truncation=True,
)
summary_ids = model.generate(
tokens_input,
min_length=80,
max_length=150,
length_penalty=20,
num_beams=2,
)
summary_gen = tokenizer.decode(
summary_ids[0], skip_special_tokens=True
)
summary.append(summary_gen)
output = pd.DataFrame(
{"text": df["text"].values.tolist(), "summary": summary}
)
if classification:
classification_token, classification_model = classify_category()
tf_batch = classification_token(
text,
max_length=128,
padding=True,
truncation=True,
return_tensors="tf",
)
with st.spinner(text="identifying theme"):
tf_outputs = classification_model(tf_batch)
classes = []
with st.spinner(text="creating output file"):
for x in stqdm(range(len(text))):
tf_o = softmax(tf_outputs["logits"][x], axis=-1)
label = np.argmax(tf_o, axis=0)
keys = model_classes
classes.append(keys.get(label))
output["category"] = classes
csv = convert_df(output)
st.download_button(
label="Download data as CSV",
data=csv,
file_name=f"{summarizer_option}_{date}_df.csv",
mime="text/csv",
)
if summarizer_option == "t5-one-line-summary":
model = SimpleT5()
load_one_line_summarizer(model=model)
summary = []
for x in stqdm(range(len(text))):
if cancel_button3.button("Cancel",key=x):
del model
break
try:
summary.append(model.predict(text[x])[0])
except:
pass
output = pd.DataFrame(
{"text": df["text"].values.tolist(), "summary": summary}
)
if classification:
classification_token, classification_model = classify_category()
tf_batch = classification_token(
text,
max_length=128,
padding=True,
truncation=True,
return_tensors="tf",
)
with st.spinner(text="identifying theme"):
tf_outputs = classification_model(tf_batch)
classes = []
with st.spinner(text="creating output file"):
for x in stqdm(range(len(text))):
tf_o = softmax(tf_outputs["logits"][x], axis=-1)
label = np.argmax(tf_o, axis=0)
keys = model_classes
classes.append(keys.get(label))
output["category"] = classes
csv = convert_df(output)
st.download_button(
label="Download data as CSV",
data=csv,
file_name=f"{summarizer_option}_{date}_df.csv",
mime="text/csv",
)
except KeyError:
st.error(
"Please Make sure that your data must have a column named text",
icon="🚨",
)
st.info("Text column must have amazon reviews", icon="ℹ️")
except BaseException as e:
logging.exception("An exception was occurred") |