Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import nltk
+from textblob import TextBlob
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
+
+# Ensure necessary NLTK datasets are downloaded
+nltk.download('punkt')
+
+# Function to analyze sentiment
+def analyze_sentiment(text):
+    analysis = TextBlob(text)
+    return analysis.sentiment.polarity  # Returns sentiment score (-1 to 1)
+
+# Function to categorize microaggressions (basic NLP)
+def categorize_microaggression(text):
+    keywords = {
+        "Microinvalidation": ["you're overreacting", "stop being so sensitive", "I don’t see color"],
+        "Microinsult": ["you’re so articulate", "where are you really from", "you must be good at math"],
+        "Microassault": ["racial slur", "explicit insult", "offensive joke"]
+    }
+    
+    for category, phrases in keywords.items():
+        for phrase in phrases:
+            if phrase in text.lower():
+                return category
+    return "Uncategorized"
+
+# Streamlit UI
+st.title("MicroAggression Insight Tool")
+st.write("Analyze, categorize, and visualize reported microaggressions.")
+
+# Collect user input
+user_input = st.text_area("Enter a microaggression example:")
+
+if st.button("Analyze"):
+    if user_input:
+        sentiment_score = analyze_sentiment(user_input)
+        category = categorize_microaggression(user_input)
+
+        # Display results
+        st.write(f"**Predicted Category:** {category}")
+        st.write(f"**Sentiment Score:** {sentiment_score:.2f} (Negative: -1, Neutral: 0, Positive: 1)")
+
+        # Store input in a dataframe
+        df = pd.DataFrame({"Text": [user_input], "Category": [category], "Sentiment": [sentiment_score]})
+        
+        # Save locally (optional)
+        df.to_csv("data.csv", mode='a', header=False, index=False)
+
+# Load existing data
+try:
+    data = pd.read_csv("data.csv", names=["Text", "Category", "Sentiment"])
+    
+    if not data.empty:
+        st.subheader("Data Insights")
+        
+        # Show category distribution
+        st.write("### Microaggression Category Distribution")
+        category_counts = data["Category"].value_counts()
+        fig, ax = plt.subplots()
+        category_counts.plot(kind='bar', ax=ax)
+        st.pyplot(fig)
+
+        # Generate a word cloud
+        st.write("### Common Words in Microaggressions")
+        wordcloud = WordCloud(width=800, height=400, background_color="white").generate(" ".join(data["Text"]))
+        fig, ax = plt.subplots()
+        ax.imshow(wordcloud, interpolation="bilinear")
+        ax.axis("off")
+        st.pyplot(fig)
+except FileNotFoundError:
+    st.write("No data available yet.")