Upload app.py

app.py

@@ -0,0 +1,156 @@
+import matplotlib as mpl
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import re
+import streamlit as st
+
+from io import StringIO, BytesIO
+from transformers import pipeline
+
+try:
+    st.set_page_config(layout="wide")
+except:
+    st.beta_set_page_config(layout="wide")
+
+st.title("Sentiment Structure Visualizer")
+
+user_input = st.text_area(
+    "Paste English text here",
+    height=200,
+)
+
+st.markdown(
+    """
+----------------------------
+"""
+)
+
+sentiment = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english",
+    tokenizer="distilbert-base-uncased-finetuned-sst-2-english",
+)
+
+def clean_text(text):
+    text = text.encode("ascii", errors="ignore").decode(
+        "ascii"
+    )  # remove non-ascii, Chinese characters
+    text = text.lower()
+    text = re.sub(r"\n", " ", text)
+    text = re.sub(r"\n\n", " ", text)
+    text = re.sub(r"\t", " ", text)
+    text = text.strip(" ")
+    text = re.sub(r"[^\w\s]", "", text)  # remove punctuation and special characters
+    text = re.sub(
+        " +", " ", text
+    ).strip()  # get rid of multiple spaces and replace with a single
+    return text
+
+if user_input != "":
+    with st.spinner("..."):
+        input_text = (
+            pd.DataFrame(user_input.split("."))
+            .stack()
+            .reset_index()
+            .rename(columns={0: "Paras"})
+            .drop("level_0", axis=1)
+            .drop("level_1", axis=1)
+        )
+
+        input_text["Clean_Text"] = input_text["Paras"].map(
+            lambda text: clean_text(text)
+        )
+
+        corpus = list(input_text["Clean_Text"].values)
+
+        input_text["Sentiment"] = sentiment(corpus)
+
+        input_text["Sentiment_Label"] = [
+            x.get("label") for x in input_text["Sentiment"]
+        ]
+
+        input_text["Sentiment_Score"] = [
+            x.get("score") for x in input_text["Sentiment"]
+        ]
+
+        cols = ["Paras", "Sentiment_Label", "Sentiment_Score"]
+        df = input_text[cols].copy()
+
+        df = df[df["Paras"].str.strip().astype(bool)]
+
+        df["Sentiment_Score"] = np.where(
+            df["Sentiment_Label"] == "NEGATIVE",
+            -(df["Sentiment_Score"]),
+            df["Sentiment_Score"],
+        )
+
+        df["Sentiment_Score"] = df["Sentiment_Score"].round(6)
+
+        overall_sentiment_score = df["Sentiment_Score"].sum().round(3)
+
+        sentiment_count = df["Sentiment_Label"].value_counts().to_string()
+
+        fig = go.Figure(
+            data=go.Heatmap(
+                z=df["Sentiment_Score"],
+                x=df.index,
+                y=df["Sentiment_Label"],
+                colorscale=px.colors.sequential.RdBu,
+            )
+        )
+
+        fig.update_layout(
+            title=go.layout.Title(text="Sentiment Sequence, By Sentence"), autosize=True
+        )
+
+        fig.update_layout(yaxis_autorange="reversed")
+
+        st.plotly_chart(fig, use_container_width=True)
+
+        buffer = StringIO()
+        fig.write_html(buffer, include_plotlyjs="cdn")
+        html_bytes = buffer.getvalue().encode()
+
+        st.download_button(
+            label="Download Interactive Chart",
+            data=html_bytes,
+            file_name="chart.html",
+            mime="text/html",
+        )
+
+col1, col2 = st.columns([1,3])
+
+with col1:
+    st.metric(
+        "Overall Sentiment Score",
+        overall_sentiment_score,
+        delta=None,
+        delta_color="normal",
+    )
+
+with col2:
+    st.metric(
+        "How Many Positive & Negative Sentences?",
+        sentiment_count,
+        delta=None,
+        delta_color="normal",
+    )
+
+
+st.markdown(
+    """
+----------------------------
+"""
+)
+
+st.subheader("Note To Users:")
+
+st.write("1. The model under the hood is distilbert-base-uncased-finetuned-sst-2-english. Clone this app and switch to another transformer model if you have a different use case.")
+
+st.write("2. This chart is interactive, and can be downloaded. Hover over the bars to see each sentence's sentiment score and label")
+
+st.write(
+    "3. You may or may not agree with the sentiment label generated for each sentence. Unfortunately there's no way to amend the output within the app."
+)