First commit

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import plotly.graph_objects as go
+
+# Load the emotion classification model
+model_id = "S-4-G-4-R/distilbert-base-uncased-finetuned-emotion"
+classifier = pipeline("text-classification", model=model_id)
+
+def classify_emotion(text):
+    """
+    Classify the emotion in the given text and return results with visualization
+    """
+    if not text.strip():
+        return None, "Please enter some text to analyze."
+    
+    # Get predictions
+    preds = classifier(text, return_all_scores=True)[0]
+    
+    # Create DataFrame
+    df = pd.DataFrame(preds)
+    df['score'] = df['score'] * 100  # Convert to percentage
+    df = df.sort_values('score', ascending=True)
+    
+    # Create horizontal bar chart using Plotly
+    fig = go.Figure(go.Bar(
+        x=df['score'],
+        y=df['label'],
+        orientation='h',
+        marker=dict(color='steelblue')
+    ))
+    
+    fig.update_layout(
+        title=f'Emotion Classification Results',
+        xaxis_title='Probability (%)',
+        yaxis_title='Emotion',
+        height=400,
+        margin=dict(l=100, r=20, t=60, b=40)
+    )
+    
+    # Format results as text
+    results_text = "**Prediction Results:**\n\n"
+    sorted_df = df.sort_values('score', ascending=False)
+    for _, row in sorted_df.iterrows():
+        results_text += f"- **{row['label']}**: {row['score']:.2f}%\n"
+    
+    return fig, results_text
+
+# Example texts
+examples = [
+    "I was feeling very alone today walking down on road",
+    "I am so happy and excited about this new opportunity!",
+    "This makes me really angry and frustrated.",
+    "I'm scared about what might happen next.",
+    "What a beautiful day, I love this!",
+    "I feel so sad and disappointed about the news."
+]
+
+# Create Gradio interface
+with gr.Blocks(title="Emotion Classifier") as demo:
+    gr.Markdown(
+        """
+        # 🎭 Emotion Classification
+        Enter any text to analyze its emotional content. The model will classify it into different emotion categories.
+        
+        **Model:** S-4-G-4-R/distilbert-base-uncased-finetuned-emotion
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Enter text to analyze",
+                placeholder="Type your text here...",
+                lines=3
+            )
+            classify_btn = gr.Button("Classify Emotion", variant="primary")
+        
+        with gr.Column():
+            results_text = gr.Markdown(label="Results")
+    
+    plot_output = gr.Plot(label="Emotion Probabilities")
+    
+    # Examples section
+    gr.Examples(
+        examples=examples,
+        inputs=text_input,
+        label="Try these examples:"
+    )
+    
+    # Connect the button
+    classify_btn.click(
+        fn=classify_emotion,
+        inputs=text_input,
+        outputs=[plot_output, results_text]
+    )
+    
+    # Also trigger on Enter key
+    text_input.submit(
+        fn=classify_emotion,
+        inputs=text_input,
+        outputs=[plot_output, results_text]
+    )
+
+# Launch the app
+demo.launch()

readme.md

@@ -0,0 +1,40 @@
+---
+title: Emotion Classifier
+emoji: 🎭
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+---
+
+# Emotion Classification App
+
+This is a Gradio web app for classifying emotions in text using the `S-4-G-4-R/distilbert-base-uncased-finetuned-emotion` model.
+
+## Features
+
+- 📝 Analyze text for emotional content
+- 📊 Interactive visualization of emotion probabilities
+- 💡 Example texts to try
+- 🎨 User-friendly interface
+
+## Model
+
+The app uses a fine-tuned DistilBERT model specifically trained for emotion classification.
+
+## Usage
+
+Simply enter any text and click "Classify Emotion" to see the emotional analysis with probability scores for different emotions.
+
+## Examples
+
+Try these sample texts:
+- "I was feeling very alone today walking down on road"
+- "I am so happy and excited about this new opportunity!"
+- "This makes me really angry and frustrated."
+
+---
+
+Built with Gradio and Transformers 🤗

requirements.txt

@@ -0,0 +1,6 @@
+
+gradio
+transformers
+torch
+plotly
+pandas