Rename app.py to sentiment-analysisapp.py

app.py

@@ -1,77 +0,0 @@
-pip install streamlit pandas numpy scikit-learn nltk
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.model_selection import train_test_split
-from sklearn.tree import DecisionTreeClassifier
-import re
-from nltk.corpus import stopwords
-from nltk.stem import SnowballStemmer
-
-# Download NLTK resources
-import nltk
-nltk.download('stopwords')
-
-# Load stopwords
-stopword = set(stopwords.words('english'))
-
-# Load dataset
-data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/twitter.csv")
-
-# Map labels
-data["labels"] = data["class"].map({0: "Hate Speech",
-                                    1: "Offensive Language",
-                                    2: "No Hate and Offensive"})
-
-# Select relevant columns
-data = data[["tweet", "labels"]]
-
-# Clean text function
-stemmer = SnowballStemmer("english")
-def clean(text):
-    text = str(text).lower()
-    text = re.sub('\[.*?\]', '', text)
-    text = re.sub('https?://\S+|www\.\S+', '', text)
-    text = re.sub('<.*?>+', '', text)
-    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
-    text = re.sub('\n', '', text)
-    text = re.sub('\w*\d\w*', '', text)
-    text = [word for word in text.split(' ') if word not in stopword]
-    text = " ".join(text)
-    text = [stemmer.stem(word) for word in text.split(' ')]
-    text = " ".join(text)
-    return text
-
-# Apply text cleaning
-data["tweet"] = data["tweet"].apply(clean)
-
-# Prepare data for model
-x = np.array(data["tweet"])
-y = np.array(data["labels"])
-
-cv = CountVectorizer()
-X = cv.fit_transform(x)  # Fit the Data
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
-
-# Train the model
-clf = DecisionTreeClassifier()
-clf.fit(X_train, y_train)
-
-# Streamlit app
-st.title("Sentiment Analysis App")
-
-# User input
-sample = st.text_area("Enter a sentence for sentiment analysis:")
-
-# Predict and display result
-if st.button("Predict"):
-    sample_cleaned = clean(sample)
-    data_sample = cv.transform([sample_cleaned]).toarray()
-    prediction = clf.predict(data_sample)[0]
-    st.success(f"Sentiment: {prediction}")
-
-# Display dataset
-st.subheader("Dataset")
-st.write(data.head())
-streamlit run sentiment_analysis_app.py

sentiment-analysisapp.py

@@ -0,0 +1,15 @@
+import streamlit as st
+from transformers import pipeline
+
+st.title("Sentiment Analysis App")
+
+# Load sentiment analysis model from Hugging Face
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+# User input
+sample = st.text_area("Enter a sentence for sentiment analysis:")
+
+# Predict and display result
+if st.button("Predict"):
+    prediction = sentiment_analyzer(sample)[0]
+    st.success(f"Sentiment: {prediction['label']} with confidence: {prediction['score']:.2f}")