Create pages/1_📈_predict.py

pages/1_📈_predict.py

@@ -0,0 +1,561 @@
+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 datetime import datetime
+import logging
+from transformers import TextClassificationPipeline
+import gc
+from datasets import load_dataset
+from utils.openllmapi.api import ChatBot
+from utils.openllmapi.exceptions import *
+import time
+from typing import List
+from collections import OrderedDict
+
+tokenizer_kwargs = dict(
+    max_length=128,
+    truncation=True,
+    padding=True,
+)
+SLEEP = 2
+
+
+def cleanMemory(obj: TextClassificationPipeline):
+    del obj
+    gc.collect()
+
+
+@st.cache_data
+def getAllCats():
+    data = load_dataset("ashhadahsan/amazon_theme")
+    data = data["train"].to_pandas()
+    labels = [x for x in list(set(data.iloc[:, 1].values.tolist())) if x != "Unknown"]
+    del data
+    return labels
+
+
+@st.cache_data
+def getAllSubCats():
+    data = load_dataset("ashhadahsan/amazon_theme")
+    data = data["train"].to_pandas()
+    labels = [x for x in list(set(data.iloc[:, 1].values.tolist())) if x != "Unknown"]
+    del data
+    return labels
+
+
+def assignHF(bot, what: str, to: str, old: List):
+    try:
+        old = ", ".join(old)
+        message_content = bot.chat(
+            f"""'Assign a one-line {what} to this summary of the text of a review
+        {to}
+        already assigned themes are , {old}
+    theme""",
+        )
+        try:
+            return message_content.split(":")[1].strip()
+        except:
+            return message_content.strip()
+    except ChatError:
+        return ""
+
+
+@st.cache_resource
+def loadZeroShotClassification():
+    classifierzero = pipeline(
+        "zero-shot-classification", model="facebook/bart-large-mnli"
+    )
+    return classifierzero
+
+
+def assignZeroShot(zero, to: str, old: List):
+    assigned = zero(to, old)
+    assigneddict = dict(zip(assigned["labels"], assigned["scores"]))
+    od = OrderedDict(sorted(assigneddict.items(), key=lambda x: x[1], reverse=True))
+    print(list(od.keys())[0])
+    print(type(list(od.keys())[0]))
+
+    return list(od.keys())[0]
+
+
+date = datetime.now().strftime(r"%Y-%m-%d")
+
+
+@st.cache_resource
+def load_t5() -> (AutoModelForSeq2SeqLM, AutoTokenizer):
+    model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+    tokenizer = AutoTokenizer.from_pretrained("t5-base")
+    return model, tokenizer
+
+
+@st.cache_resource
+def summarizationModel():
+    return pipeline("summarization", model="my_awesome_sum/")
+
+
+@st.cache_resource
+def convert_df(df: pd.DataFrame):
+    # IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df.to_csv(index=False).encode("utf-8")
+
+
+def load_one_line_summarizer(model):
+    return model.load_model("t5", "snrspeaks/t5-one-line-summary")
+
+
+@st.cache_resource
+def classify_theme() -> TextClassificationPipeline:
+    tokenizer = BertTokenizer.from_pretrained(
+        "ashhadahsan/amazon-theme-bert-base-finetuned"
+    )
+    model = TFBertForSequenceClassification.from_pretrained(
+        "ashhadahsan/amazon-theme-bert-base-finetuned"
+    )
+    pipeline = TextClassificationPipeline(
+        model=model, tokenizer=tokenizer, top_k=1, **tokenizer_kwargs
+    )
+    return pipeline
+
+
+@st.cache_resource
+def classify_sub_theme() -> TextClassificationPipeline:
+    tokenizer = BertTokenizer.from_pretrained(
+        "ashhadahsan/amazon-subtheme-bert-base-finetuned"
+    )
+    model = TFBertForSequenceClassification.from_pretrained(
+        "ashhadahsan/amazon-subtheme-bert-base-finetuned"
+    )
+    pipeline = TextClassificationPipeline(
+        model=model, tokenizer=tokenizer, top_k=1, **tokenizer_kwargs
+    )
+    return pipeline
+
+
+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"])
+
+try:
+    bot = ChatBot(
+        cookies={
+            "hf-chat": st.secrets["hf-chat"],
+            "token": st.secrets["token"],
+        }
+    )
+except ChatBotInitError as e:
+    print(e)
+
+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)
+
+treshold = st.slider(
+    label="Model Confidence value",
+    min_value=0.1,
+    max_value=0.8,
+    step=0.1,
+    value=0.6,
+    help="If the model has a confidence score below this number , then a new label is assigned (0.6) means 60 percent and so on",
+)
+
+ps = st.empty()
+
+if st.button("Process", type="primary"):
+    themes = getAllCats()
+    subthemes = getAllSubCats()
+    # st.write(themes)
+
+    oneline = SimpleT5()
+    load_one_line_summarizer(model=oneline)
+    zeroline = loadZeroShotClassification()
+
+    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)
+        outputdf = pd.DataFrame()
+        try:
+            text = df["text"].values.tolist()[0:100]
+            outputdf["text"] = text
+            if summarizer_option == "Custom trained on the dataset":
+                if summary_yes:
+                    model = summarizationModel()
+
+                    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
+                    outputdf["summary"] = summary
+                    del model
+                if classification:
+                    themePipe = classify_theme()
+                    classes = []
+                    classesUnlabel = []
+                    classesUnlabelZero = []
+                    for x in stqdm(
+                        text,
+                        desc="Assigning Themes ...",
+                        total=len(text),
+                        colour="#BF1A1A",
+                    ):
+                        output = themePipe(x)[0][0]["label"]
+                        classes.append(output)
+                        score = round(themePipe(x)[0][0]["score"], 2)
+                        if score <= treshold:
+                            onelineoutput=oneline.predict(x)[0]
+                            time.sleep(SLEEP)
+                            print("hit")
+                            classesUnlabel.append(
+                                assignHF(
+                                    bot=bot,
+                                    what="theme",
+                                    to=onelineoutput,
+                                    old=themes,
+                                )
+                            ) 
+                            classesUnlabelZero.append(
+                                assignZeroShot(
+                                    zero=zeroline, to=onelineoutput, old=themes
+                                )
+                            )
+
+                        else:
+                            classesUnlabel.append("")
+                            classesUnlabelZero.append("")
+
+                    outputdf["Review Theme"] = classes
+                    outputdf["Review Theme-issue-new"] = classesUnlabel
+                    outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
+                    cleanMemory(themePipe)
+                if sub_theme:
+                    subThemePipe = classify_sub_theme()
+                    classes = []
+                    classesUnlabel = []
+                    classesUnlabelZero = []
+                    for x in stqdm(
+                        text,
+                        desc="Assigning Subthemes ...",
+                        total=len(text),
+                        colour="green",
+                    ):
+                        output = subThemePipe(x)[0][0]["label"]
+                        classes.append(output)
+                        score = round(subThemePipe(x)[0][0]["score"], 2)
+                        if score <= treshold:
+                            onelineoutput=oneline.predict(x)[0]
+
+                            time.sleep(SLEEP)
+
+                            print("hit")
+                            classesUnlabel.append(
+                                assignHF(
+                                    bot=bot,
+                                    what="subtheme",
+                                    to=onelineoutput,
+                                    old=subthemes,
+                                )
+                            )
+                            classesUnlabelZero.append(
+                                assignZeroShot(
+                                    zero=zeroline,
+                                    to=onelineoutput,
+                                    old=subthemes,
+                                )
+                            )
+
+                        else:
+                            classesUnlabel.append("")
+                            classesUnlabelZero.append("")
+
+                    outputdf["Review SubTheme"] = classes
+                    outputdf["Review SubTheme-issue-new"] = classesUnlabel
+                    outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
+
+                    cleanMemory(subThemePipe)
+
+                csv = convert_df(outputdf)
+                st.download_button(
+                    label="Download output as CSV",
+                    data=csv,
+                    file_name=f"{summarizer_option}_{date}_df.csv",
+                    mime="text/csv",
+                    use_container_width=True,
+                )
+            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
+                    outputdf["summary"] = summary
+
+                if classification:
+                    themePipe = classify_theme()
+                    classes = []
+                    classesUnlabel = []
+                    classesUnlabelZero = []
+                    for x in stqdm(
+                        text, desc="Assigning Themes ...", total=len(text), colour="red"
+                    ):
+                        output = themePipe(x)[0][0]["label"]
+                        classes.append(output)
+                        score = round(themePipe(x)[0][0]["score"], 2)
+                        if score <= treshold:
+                            onelineoutput=oneline.predict(x)[0]
+
+                            print("hit")
+                            time.sleep(SLEEP)
+
+                            classesUnlabel.append(
+                                assignHF(
+                                    bot=bot,
+                                    what="theme",
+                                    to=onelineoutput,
+                                    old=themes,
+                                )
+                            )
+                            classesUnlabelZero.append(
+                                assignZeroShot(
+                                    zero=zeroline, to=onelineoutput, old=themes
+                                )
+                            )
+
+                        else:
+                            classesUnlabel.append("")
+                            classesUnlabelZero.append("")
+                    outputdf["Review Theme"] = classes
+                    outputdf["Review Theme-issue-new"] = classesUnlabel
+                    outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
+                    cleanMemory(themePipe)
+
+                if sub_theme:
+                    subThemePipe = classify_sub_theme()
+                    classes = []
+                    classesUnlabelZero = []
+
+                    for x in stqdm(
+                        text,
+                        desc="Assigning Subthemes ...",
+                        total=len(text),
+                        colour="green",
+                    ):
+                        output = subThemePipe(x)[0][0]["label"]
+                        classes.append(output)
+                        score = round(subThemePipe(x)[0][0]["score"], 2)
+                        if score <= treshold:
+                            onelineoutput=oneline.predict(x)[0]
+
+                            time.sleep(SLEEP)
+                            print("hit")
+                            classesUnlabel.append(
+                                assignHF(
+                                    bot=bot,
+                                    what="subtheme",
+                                    to=onelineoutput,
+                                    old=subthemes,
+                                )
+                            )
+                            classesUnlabelZero.append(
+                                assignZeroShot(
+                                    zero=zeroline,
+                                    to=onelineoutput,
+                                    old=subthemes,
+                                )
+                            )
+
+                        else:
+                            classesUnlabel.append("")
+                            classesUnlabelZero.append("")
+
+                    outputdf["Review SubTheme"] = classes
+                    outputdf["Review SubTheme-issue-new"] = classesUnlabel
+                    outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
+
+                    cleanMemory(subThemePipe)
+
+                csv = convert_df(outputdf)
+                st.download_button(
+                    label="Download output as CSV",
+                    data=csv,
+                    file_name=f"{summarizer_option}_{date}_df.csv",
+                    mime="text/csv",
+                    use_container_width=True,
+                )
+
+            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
+                    outputdf["summary"] = summary
+                    del model
+
+                if classification:
+                    themePipe = classify_theme()
+                    classes = []
+                    classesUnlabel = []
+                    classesUnlabelZero = []
+                    for x in stqdm(
+                        text, desc="Assigning Themes ...", total=len(text), colour="red"
+                    ):
+                        output = themePipe(x)[0][0]["label"]
+                        classes.append(output)
+                        score = round(themePipe(x)[0][0]["score"], 2)
+                        if score <= treshold:
+                            onelineoutput=oneline.predict(x)[0]
+
+                            time.sleep(SLEEP)
+
+                            print("hit")
+                            classesUnlabel.append(
+                                assignHF(
+                                    bot=bot,
+                                    what="theme",
+                                    to=onelineoutput,
+                                    old=themes,
+                                )
+                            )
+                            classesUnlabelZero.append(
+                                assignZeroShot(
+                                    zero=zeroline, to=onelineoutput, old=themes
+                                )
+                            )
+
+                        else:
+                            classesUnlabel.append("")
+                            classesUnlabelZero.append("")
+                    outputdf["Review Theme"] = classes
+                    outputdf["Review Theme-issue-new"] = classesUnlabel
+                    outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
+
+                if sub_theme:
+                    subThemePipe = classify_sub_theme()
+                    classes = []
+                    classesUnlabelZero = []
+
+                    for x in stqdm(
+                        text,
+                        desc="Assigning Subthemes ...",
+                        total=len(text),
+                        colour="green",
+                    ):
+                        output = subThemePipe(x)[0][0]["label"]
+                        classes.append(output)
+                        score = round(subThemePipe(x)[0][0]["score"], 2)
+                        if score <= treshold:
+                            print("hit")
+                            onelineoutput=oneline.predict(x)[0]
+
+                            time.sleep(SLEEP)
+                            classesUnlabel.append(
+                                assignHF(
+                                    bot=bot,
+                                    what="subtheme",
+                                    to=onelineoutput,
+                                    old=subthemes,
+                                )
+                            )
+                            classesUnlabelZero.append(
+                                assignZeroShot(
+                                    zero=zeroline,
+                                    to=onelineoutput,
+                                    old=subthemes,
+                                )
+                            )
+
+                        else:
+                            classesUnlabel.append("")
+                            classesUnlabelZero.append("")
+
+                    outputdf["Review SubTheme"] = classes
+                    outputdf["Review SubTheme-issue-new"] = classesUnlabel
+                    outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
+
+                    cleanMemory(subThemePipe)
+
+                csv = convert_df(outputdf)
+                st.download_button(
+                    label="Download output as CSV",
+                    data=csv,
+                    file_name=f"{summarizer_option}_{date}_df.csv",
+                    mime="text/csv",
+                    use_container_width=True,
+                )
+
+        except KeyError as e:
+            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")
+            # st.exception(e)
+
+        except BaseException as e:
+            logging.exception("An exception was occurred")