Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from textblob import TextBlob
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import os
+from wordcloud import WordCloud
+
+# Function to analyze sentiment using the custom Hugging Face pipeline
+def analyze_sentiment_hf(text):
+    hf_pipeline = pipeline("sentiment-analysis", "RohitBh/Sentimental_Analysis")
+    if len(text) > 512:
+        text = text[:511]
+    sentiment_result = hf_pipeline(text)
+    sentiment_label = sentiment_result[0]["label"]
+    if sentiment_label == "LABEL_1":
+        return "Positive"
+    elif sentiment_label == "LABEL_0":
+        return "Negative"
+    else:
+        return "Neutral"
+
+# Function to analyze sentiment using VADER
+def analyze_sentiment_vader(text):
+    sentiment_analyzer = SentimentIntensityAnalyzer()
+    sentiment_score = sentiment_analyzer.polarity_scores(text)["compound"]
+    if sentiment_score > 0:
+        return "Positive"
+    elif sentiment_score == 0:
+        return "Neutral"
+    else:
+        return "Negative"
+
+# Function to analyze sentiment using TextBlob
+def analyze_sentiment_textblob(text):
+    sentiment_analysis = TextBlob(text)
+    score = sentiment_analysis.sentiment.polarity
+    if score > 0:
+        return "Positive"
+    elif score == 0:
+        return "Neutral"
+    else:
+        return "Negative"
+
+# Function to display DataFrame with sentiment
+def display_results_dataframe(data_frame):
+    st.write(data_frame)
+
+# Function to display a pie chart of sentiment distribution
+def create_pie_chart(data_frame, sentiment_column):
+    sentiment_distribution = data_frame[sentiment_column].value_counts()
+    fig, ax = plt.subplots()
+    ax.pie(sentiment_distribution, labels=sentiment_distribution.index, autopct='%1.1f%%', startangle=90)
+    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+    st.pyplot(fig)
+
+# Function to display word cloud based on sentiment data
+def create_word_cloud(sentiment_data):
+    wordcloud_generator = WordCloud(width=800, height=400).generate(sentiment_data)
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ax.imshow(wordcloud_generator, interpolation='bilinear')
+    ax.axis('off')
+    st.pyplot(fig)
+
+# Main UI setup
+st.set_page_config(page_title="Sentiment Analysis Tool", page_icon=":bar_chart:")
+st.title("Sentiment Analysis Tool")
+
+# Sidebar configuration for user input options
+st.sidebar.title("Analysis Options")
+input_type = st.sidebar.selectbox("Choose Input Type", ["Text Input", "CSV Upload"])
+model_choice = st.sidebar.selectbox("Choose Sentiment Analysis Model", ["Hugging Face", "VADER", "TextBlob"])
+display_type = st.sidebar.selectbox("Choose Display Type", ["DataFrame", "Pie Chart", "Word Cloud"])
+
+# Process input based on user choice
+if input_type == "Text Input":
+    user_text = st.text_input("Enter text for sentiment analysis:")
+    if st.button("Analyze Sentiment"):
+        if user_text:
+            # Analyzing sentiment based on selected model
+            if model_choice == "Hugging Face":
+                sentiment = analyze_sentiment_hf(user_text)
+            elif model_choice == "VADER":
+                sentiment = analyze_sentiment_vader(user_text)
+            else:
+                sentiment = analyze_sentiment_textblob(user_text)
+            
+            st.write("Detected Sentiment:", sentiment)
+        else:
+            st.warning("Please enter some text to analyze.")
+elif input_type == "CSV Upload":
+    uploaded_file = st.file_uploader("Upload CSV file for analysis", type="csv")
+    if st.button("Start Analysis"):
+        if uploaded_file is not None:
+            data_frame = pd.read_csv(uploaded_file)
+            # Assuming the CSV has a column named 'text' for analysis
+            if 'text' in data_frame.columns:
+                data_frame['Sentiment'] = data_frame['text'].apply(lambda x: analyze_sentiment_hf(x) if model_choice == "Hugging Face" else (analyze_sentiment_vader(x) if model_choice == "VADER" else analyze_sentiment_textblob(x)))
+                
+                if display_type == "DataFrame":
+                    display_results_dataframe(data_frame)
+                elif display_type == "Pie Chart":
+                    create_pie_chart(data_frame, 'Sentiment')
+                elif display_type == "Word Cloud":
+                    combined_text = ' '.join(data_frame['text'])
+                    create_word_cloud(combined_text)
+            else:
+                st.error("The uploaded CSV file must contain a 'text' column.")
+        else:
+            st.warning("Please upload a CSV file to proceed with analysis.")