Upload app.py

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import re
+
+from transformers import pipeline
+
+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
+
+
+# note that the sentiment-analysis pipeline returns 2 values - a label and a score
+def sentiment_analysis(text):
+    input_text = (
+        pd.DataFrame(text.split("."))
+        .stack()
+        .reset_index()
+        .rename(columns={0: "Paras"})
+        .drop("level_0", axis=1)
+        .drop("level_1", axis=1)
+        .dropna()
+    )
+
+    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()
+
+    return overall_sentiment_score, sentiment_count, df
+
+
+gradio_ui = gr.Interface(
+    fn=sentiment_analysis,
+    title="Analyse The Sentiment Structure Of A Speech",
+    description="Upload a speech or parts of it for a detailed sentiment analysis",
+    inputs=gr.inputs.Textbox(lines=30, label="Paste Text Here"),
+    outputs=[
+        gr.outputs.Textbox(type="number", label="Overall Sentiment Score"),
+        gr.outputs.Textbox(
+            type="auto", label="How Many Positive & Negative Sentences?"
+        ),
+        gr.outputs.Dataframe(
+            headers=["Paras", "Sentiment_Label", "Sentiment_Score"],
+            max_rows=None,
+            max_cols=3,
+            overflow_row_behaviour="paginate",
+            type="auto",
+            label="Detailed Assessment By Sentence",
+        ),
+    ],
+    enable_queue=True,
+)
+
+
+gradio_ui.launch()