Create app.py

app.py

@@ -0,0 +1,170 @@
+
+import streamlit as st
+import numpy as np
+import pandas as pd
+import json
+import base64
+import uuid
+from pandas import DataFrame
+import time
+import re
+
+def download_button(object_to_download, download_filename, button_text):
+
+    if isinstance(object_to_download, bytes):
+        pass
+
+    elif isinstance(object_to_download, pd.DataFrame):
+        object_to_download = object_to_download.to_csv(index=False)
+    # Try JSON encode for everything else
+    else:
+        object_to_download = json.dumps(object_to_download)
+
+    try:
+        # some strings <-> bytes conversions necessary here
+        b64 = base64.b64encode(object_to_download.encode()).decode()
+    except AttributeError as e:
+        b64 = base64.b64encode(object_to_download).decode()
+
+    button_uuid = str(uuid.uuid4()).replace("-", "")
+    button_id = re.sub("\d+", "", button_uuid)
+
+    custom_css = f"""
+    <style>
+    #{button_id} {{
+                     display: inline-flex;
+                     align-items: center;
+                     justify-content: center;
+                     background-color: rgb(255, 255, 255);
+                     color: rgb(38, 39, 48);
+                     padding: .25rem .75rem;
+                     position: relative;
+                     text-decoration: none;
+                     border-radius: 4px;
+                     border-width: 1px;
+                     border-style: solid;
+                     border-color: rgb(230, 234, 241);
+                     border-image: initial;
+                     }}
+                     #{button_id}:hover {{
+                border-color: rgb(246, 51, 102);
+                color: rgb(246, 51, 102);
+                }}
+                #{button_id}:active {{
+                                 box-shadow: none;
+                                 background-color: rgb(246, 51, 102);
+                                 color: white;
+                                 }}
+                                 </style> """
+
+    dl_link = (
+        custom_css
+        + f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br><br>'
+    )
+    # dl_link = f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}"><input type="button" kind="primary" value="{button_text}"></a><br></br>'
+
+    st.markdown(dl_link, unsafe_allow_html=True)
+
+
+
+class c_model:
+    def __init__(self):
+        # st.write('my model')
+        pass
+
+    @st.cache
+    def load_model(self, name_or_path):
+        time.sleep(3)
+        return None
+
+    def predict(self, texts):
+        return np.random.randint(2), np.random.rand()
+
+
+st.title('Sentiment Analysis')
+
+
+# Load classification model
+with st.spinner('Loading classification model...'):
+    from transformers import pipeline
+
+    checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews"
+    classifier = pipeline("text-classification", model=checkpoint)
+
+
+tab1, tab2 = st.tabs(["Single Comment", "Multiple Comment"])
+
+with tab1:
+    st.subheader('Single comment classification')
+    text_input = st.text_area(label='Paste your text below (max 256 words)',
+                              value='Hiiiiiiiii')
+    MAX_WORDS = 256
+    res = len(re.findall(r"\w+", text_input))
+    if res > MAX_WORDS:
+        st.warning(
+            "⚠️ Your text contains "
+            + str(res)
+            + " words."
+            + " Only the first 256 words will be reviewed! 😊"
+        )
+        text_input = text_input[:MAX_WORDS]
+
+    submit_button = st.button(label='Submit comment')
+    if submit_button:
+        with st.spinner('Predicting ...'):
+            start_time = time.time()
+            time.sleep(2)
+            preds = classifier([text_input])[0]
+            end_time = time.time()
+            p_time = round(end_time-start_time, 2)
+            st.success(f'Prediction finished in {p_time}s!')
+
+            st.write(f'Label: {preds["label"]}, with certainty: {preds["score"]}')
+
+
+with tab2:
+    st.subheader('Multiple comment classification')
+    file_input = st.file_uploader(label='Choose a file:', type='csv')
+    if file_input:
+        try:
+            df = pd.read_csv(file_input)
+            texts = df['text'].to_list()
+        except:
+            st.write('Bad File Error...')
+
+        st.write(f"First 5 rows of {file_input.name} texts")
+        st.write(texts[:5])
+
+        submit_button = st.button(label='Submit file')
+        if submit_button:
+            with st.spinner('Predicting ...'):
+                start_time = time.time()
+                time.sleep(2)
+                preds = classifier(texts)
+                end_time = time.time()
+                p_time = round(end_time-start_time, 2)
+                st.success(f'Prediction finished in {p_time}s!')
+
+            c1, c2 = st.columns([3, 1])
+            with c1:
+                st.subheader("🎈 Check & download results")
+            with c2:
+                CSVButton2 = download_button(results, "Data.csv", "📥 Download (.csv)")
+
+            st.header("")
+
+            for text, pred in zip(texts, preds):
+                pred['text'] = text
+
+            df = pd.DataFrame(preds, columns=['text', 'label', 'score'])
+
+            import seaborn as sns
+            # Add styling
+            cmGreen = sns.light_palette("green", as_cmap=True)
+            cmRed = sns.light_palette("red", as_cmap=True)
+            df = df.style.background_gradient(
+                cmap=cmGreen,
+                subset=["score"],
+            )
+
+            st.table(df)