Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the Hugging Face pipelines
+classifier = pipeline("text-classification", model="bhadresh-savani/bert-base-go-emotion")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Streamlit app UI
+st.title("Emotion Detection and Comment Summarization")
+st.markdown(
+    """
+    This app detects the emotion in a given comment and provides a concise summary.
+    """
+)
+
+# Input text box for comments
+comment_input = st.text_area(
+    "Enter your comment:",
+    placeholder="Type your comment here...",
+    height=200
+)
+
+# Analyze button
+if st.button("Analyze Comment"):
+    if not comment_input.strip():
+        st.error("Please provide a valid comment.")
+    else:
+        # Perform emotion classification
+        emotion_result = classifier(comment_input)[0]
+        emotion_label = emotion_result["label"]
+        emotion_score = round(emotion_result["score"], 4)
+
+        # Perform summarization
+        summary_result = summarizer(comment_input, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
+
+        # Display results
+        st.subheader("Analysis Result")
+        st.write(f"### **Emotion:** {emotion_label} (Confidence: {emotion_score})")
+        st.write(f"### **Summary:** {summary_result}")