Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import plotly.graph_objects as go
+
+# Page config
+st.set_page_config(
+    page_title="Emotion Detector",
+    page_icon="📊",
+    layout="wide"
+)
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+    model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+    return tokenizer, model
+
+def analyze_text(text, tokenizer, model):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return probs[0].detach().numpy()
+
+def create_emotion_plot(emotions_dict):
+    fig = go.Figure(data=[
+        go.Bar(
+            x=list(emotions_dict.keys()),
+            y=list(emotions_dict.values()),
+            marker_color=['#FF9999', '#99FF99', '#9999FF', '#FFFF99', '#FF99FF', '#99FFFF', '#FFB366']
+        )
+    ])
+    
+    fig.update_layout(
+        title="Emotion Analysis Results",
+        xaxis_title="Emotions",
+        yaxis_title="Confidence Score",
+        yaxis_range=[0, 1]
+    )
+    return fig
+
+# App title and description
+st.title("📊 Text Emotion Analysis")
+st.markdown("""
+This app analyzes the emotional content of your text using a pre-trained emotion detection model.
+Try typing or pasting some text below!
+""")
+
+# Load model
+with st.spinner("Loading model..."):
+    tokenizer, model = load_model()
+
+# Define emotions
+emotions = ['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise']
+
+# Text input
+text_input = st.text_area("Enter your text here:", height=150)
+
+# Add example button
+if st.button("Try an example"):
+    text_input = "I just got the best news ever! I'm so excited and happy I can hardly contain myself! 🎉"
+    st.text_area("Enter your text here:", value=text_input, height=150)
+
+if st.button("Analyze Emotions"):
+    if text_input.strip() == "":
+        st.warning("Please enter some text to analyze.")
+    else:
+        with st.spinner("Analyzing emotions..."):
+            # Get predictions
+            probs = analyze_text(text_input, tokenizer, model)
+            emotions_dict = dict(zip(emotions, probs))
+            
+            # Display results
+            st.subheader("Analysis Results")
+            
+            # Create columns for layout
+            col1, col2 = st.columns([2, 1])
+            
+            with col1:
+                # Display plot
+                fig = create_emotion_plot(emotions_dict)
+                st.plotly_chart(fig, use_container_width=True)
+            
+            with col2:
+                # Display scores
+                st.subheader("Emotion Scores:")
+                for emotion, score in emotions_dict.items():
+                    st.write(f"{emotion.capitalize()}: {score:.2%}")
+
+# Add footer
+st.markdown("---")
+st.markdown("""
+Created with ❤️ using Hugging Face Transformers and Streamlit.
+Model: j-hartmann/emotion-english-distilroberta-base
+""")