Delete app.py

app.py

@@ -1,340 +0,0 @@
-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, TFBertForSequenceClassification
-from tensorflow.keras.models import load_model
-from tensorflow.nn import softmax
-import numpy as np
-from datetime import datetime
-import logging
-from constants import sub_themes_dict
-
-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)
-# @st.cache_resource
-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)
-# @st.cache_resource
-def custom_model():
-    return pipeline("summarization", model="my_awesome_sum/")
-
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-# @st.cache_resource
-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)
-# @st.cache_resource
-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)
-# @st.cache_resource
-def classify_category():
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    new_model = load_model("model")
-    return tokenizer, new_model
-
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-# @st.cache_resource
-def classify_sub_theme():
-    tokenizer = BertTokenizer.from_pretrained(
-        "ashhadahsan/amazon-subtheme-bert-base-finetuned"
-    )
-    new_model = TFBertForSequenceClassification.from_pretrained(
-        "ashhadahsan/amazon-subtheme-bert-base-finetuned"
-    )
-    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"),
-)
-col1, col2, col3 = st.columns([1, 1, 1])
-
-with col1:
-    summary_yes = st.checkbox("Summrization", value=False)
-
-with col2:
-    classification = st.checkbox("Classify Category", value=True)
-
-with col3:
-    sub_theme = st.checkbox("Sub theme classification", 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)
-        output = pd.DataFrame()
-        try:
-            text = df["text"].values.tolist()
-            output["text"] = text
-            if summarizer_option == "Custom trained on the dataset":
-                if summary_yes:
-                    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["summary"] = summary
-                    del model
-                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
-                    del classification_token, classification_model
-                if sub_theme:
-                    classification_token, classification_model = classify_sub_theme()
-                    tf_batch = classification_token(
-                        text,
-                        max_length=128,
-                        padding=True,
-                        truncation=True,
-                        return_tensors="tf",
-                    )
-                    with st.spinner(text="identifying sub 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 = sub_themes_dict
-                            classes.append(keys.get(label))
-                        output["sub theme"] = classes
-                    del classification_token, classification_model
-
-                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":
-                if summary_yes:
-                    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)
-                    del model, tokenizer
-                    output["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
-                    del classification_token, classification_model
-                if sub_theme:
-                    classification_token, classification_model = classify_sub_theme()
-                    tf_batch = classification_token(
-                        text,
-                        max_length=128,
-                        padding=True,
-                        truncation=True,
-                        return_tensors="tf",
-                    )
-                    with st.spinner(text="identifying sub 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 = sub_themes_dict
-                            classes.append(keys.get(label))
-                        output["sub theme"] = classes
-                    del classification_token, classification_model
-                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":
-                if summary_yes:
-                    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["summary"] = summary
-                    del model
-
-                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
-                    del classification_token, classification_model
-                if sub_theme:
-                    classification_token, classification_model = classify_sub_theme()
-                    tf_batch = classification_token(
-                        text,
-                        max_length=128,
-                        padding=True,
-                        truncation=True,
-                        return_tensors="tf",
-                    )
-                    with st.spinner(text="identifying sub 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 = sub_themes_dict
-                            classes.append(keys.get(label))
-                        output["sub theme"] = classes
-                    del classification_token, classification_model
-
-                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")