Create app.py

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import io
+import json
+
+# Load the emotion classification model
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "text-classification", 
+        model="j-hartmann/emotion-english-distilroberta-base", 
+        return_all_scores=True
+    )
+
+emotion_classifier = load_model()
+
+# Define a function to predict emotions and generate a bar chart
+def predict_emotion_with_chart(text):
+    if not text.strip():
+        return None, None
+
+    # Get predictions
+    results = emotion_classifier(text)
+    emotions = {result['label']: round(result['score'], 2) for result in results[0]}
+    sorted_emotions = dict(sorted(emotions.items(), key=lambda item: item[1], reverse=True))
+
+    # Create a bar chart
+    fig, ax = plt.subplots(figsize=(6, 4))
+    ax.bar(sorted_emotions.keys(), sorted_emotions.values(), color="skyblue")
+    ax.set_title("Emotion Scores")
+    ax.set_ylabel("Confidence Score")
+    ax.set_ylim(0, 1)
+    ax.set_xticklabels(sorted_emotions.keys(), rotation=45, ha="right")
+    plt.tight_layout()
+
+    return sorted_emotions, fig
+
+# Function to generate JSON result
+def generate_json(text):
+    results = emotion_classifier(text)
+    emotions = {result['label']: round(result['score'], 2) for result in results[0]}
+    return json.dumps(emotions, indent=2)
+
+# Streamlit UI
+st.title("🌟 Enhanced Emotion Detection App")
+st.markdown("Analyze the emotions in a sentence and visualize them. Enter text to detect emotions, see a bar chart of scores, and download the results as a JSON file.")
+
+# Input text
+text_input = st.text_area("Enter text to analyze emotions:", "")
+
+if st.button("Analyze Emotions"):
+    if text_input.strip():
+        emotion_scores, chart = predict_emotion_with_chart(text_input)
+        if emotion_scores:
+            st.subheader("Emotion Scores")
+            st.json(emotion_scores)
+            st.subheader("Emotion Chart")
+            st.pyplot(chart)
+        else:
+            st.error("No emotions detected. Please enter a valid text.")
+    else:
+        st.warning("Please enter some text.")
+
+# Download JSON results
+if text_input.strip():
+    json_data = generate_json(text_input)
+    st.download_button(label="Download Results as JSON", data=json_data, file_name="emotion_results.json", mime="application/json")