app.py

import gradio as gr
import pandas as pd
import plotly.express as px
import torch
from huggingface_hub import hf_hub_download
from importlib import import_module
import shutil
import os

# Load inference.py and model
repo_id = "logasanjeev/emotion-analyzer-bert"
local_file = hf_hub_download(repo_id=repo_id, filename="inference.py")
print("Downloaded inference.py successfully!")

current_dir = os.getcwd()
destination = os.path.join(current_dir, "inference.py")
shutil.copy(local_file, destination)
print("Copied inference.py to current directory!")

inference_module = import_module("inference")
predict_emotions = inference_module.predict_emotions
print("Imported predict_emotions successfully!")

_, _ = predict_emotions("dummy text")
emotion_labels = inference_module.EMOTION_LABELS
default_thresholds = inference_module.THRESHOLDS

# Prediction function with grouped bar chart
def predict_emotions_with_details(text, confidence_threshold=0.0):
    if not text.strip():
        return "Please enter some text.", "", "", None
    
    predictions_str, processed_text = predict_emotions(text)
    
    # Parse predictions
    predictions = []
    if predictions_str != "No emotions predicted.":
        for line in predictions_str.split("\n"):
            emotion, confidence = line.split(": ")
            predictions.append((emotion, float(confidence)))
    
    # Get raw logits for all emotions (for Top 5)
    encodings = inference_module.TOKENIZER(
        processed_text,
        padding='max_length',
        truncation=True,
        max_length=128,
        return_tensors='pt'
    )
    input_ids = encodings['input_ids'].to(inference_module.DEVICE)
    attention_mask = encodings['attention_mask'].to(inference_module.DEVICE)
    
    with torch.no_grad():
        outputs = inference_module.MODEL(input_ids, attention_mask=attention_mask)
        logits = torch.sigmoid(outputs.logits).cpu().numpy()[0]
    
    # All emotions for Top 5
    all_emotions = [(emotion_labels[i], round(logit, 4)) for i, logit in enumerate(logits)]
    all_emotions.sort(key=lambda x: x[1], reverse=True)
    top_5_emotions = all_emotions[:5]
    top_5_output = "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in top_5_emotions])
    
    # Filter predictions based on threshold
    filtered_predictions = []
    for emotion, confidence in predictions:
        thresh = default_thresholds[emotion_labels.index(emotion)]
        adjusted_thresh = max(thresh, confidence_threshold)
        if confidence >= adjusted_thresh:
            filtered_predictions.append((emotion, confidence))
    
    if not filtered_predictions:
        thresholded_output = "No emotions predicted above thresholds."
    else:
        thresholded_output = "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in filtered_predictions])
    
    # Create grouped bar chart
    fig = None
    if filtered_predictions or top_5_emotions:
        emotions = set([pred[0] for pred in filtered_predictions] + [emo[0] for emo in top_5_emotions])
        thresholded_dict = {pred[0]: pred[1] for pred in filtered_predictions}
        top_5_dict = {emo[0]: emo[1] for emo in top_5_emotions}
        
        data = {
            "Emotion": [],
            "Confidence": [],
            "Category": []
        }
        
        for emotion in emotions:
            if emotion in thresholded_dict:
                data["Emotion"].append(emotion)
                data["Confidence"].append(thresholded_dict[emotion])
                data["Category"].append("Above Threshold")
            if emotion in top_5_dict:
                data["Emotion"].append(emotion)
                data["Confidence"].append(top_5_dict[emotion])
                data["Category"].append("Top 5")
        
        df = pd.DataFrame(data)
        
        fig = px.bar(
            df,
            x="Emotion",
            y="Confidence",
            color="Category",
            barmode="group",
            title="Emotion Confidence Comparison",
            height=400,
            color_discrete_map={"Above Threshold": "#ff6b6b", "Top 5": "#4ecdc4"}
        )
        fig.update_traces(texttemplate='%{y:.2f}', textposition='auto')
        fig.update_layout(
            margin=dict(t=50, b=50),
            xaxis_title="",
            yaxis_title="Confidence",
            legend_title="",
            legend=dict(orientation="h", yanchor="bottom", y=1.05, xanchor="center", x=0.5),
            plot_bgcolor="rgba(0,0,0,0)",
            paper_bgcolor="rgba(0,0,0,0)",
            font=dict(color="#e0e0e0")
        )
    
    return processed_text, thresholded_output, top_5_output, fig

# Enhanced CSS with vibrant colors, animations, and better UX
custom_css = """
body {
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
    background: linear-gradient(145deg, #1a1a3d 0%, #2e2e5c 100%);
    color: #e6e6fa;
    margin: 0;
    padding: 20px;
    min-height: 100vh;
}
.gr-panel {
    border-radius: 12px;
    box-shadow: 0 6px 20px rgba(0,0,0,0.25);
    background: rgba(255, 255, 255, 0.08);
    backdrop-filter: blur(8px);
    padding: 25px;
    margin: 20px auto;
    max-width: 900px;
    border: 1px solid rgba(255, 255, 255, 0.15);
    transition: transform 0.3s ease;
}
.gr-panel:hover {
    transform: translateY(-5px);
}
.gr-button {
    border-radius: 8px;
    padding: 12px 30px;
    font-weight: 600;
    background: linear-gradient(90deg, #ff6b6b 0%, #ff8e53 100%);
    color: white;
    border: none;
    transition: all 0.3s ease;
    cursor: pointer;
    margin-top: 15px;
}
.gr-button:hover {
    background: linear-gradient(90deg, #ff8e53 0%, #ff6b6b 100%);
    transform: scale(1.05);
    box-shadow: 0 4px 15px rgba(255, 107, 107, 0.4);
}
.gr-textbox, .gr-slider {
    margin-bottom: 20px;
}
.gr-textbox label, .gr-slider label {
    font-size: 1.1em;
    font-weight: 600;
    color: #e6e6fa;
    margin-bottom: 8px;
    display: block;
}
.gr-textbox textarea, .gr-textbox input {
    border: 1px solid rgba(255, 255, 255, 0.2);
    border-radius: 6px;
    padding: 10px;
    font-size: 1em;
    background: rgba(255, 255, 255, 0.1);
    color: #e6e6fa;
    transition: border-color 0.3s ease;
}
.gr-textbox textarea:focus, .gr-textbox input:focus {
    border-color: #ff6b6b;
    outline: none;
}
#title {
    font-size: 2.2em;
    font-weight: 700;
    color: #ffffff;
    text-align: center;
    margin: 40px 0 15px 0;
    text-shadow: 0 2px 4px rgba(0,0,0,0.3);
}
#description {
    font-size: 1.1em;
    color: #d3d3fa;
    text-align: center;
    max-width: 700px;
    margin: 0 auto 40px auto;
    line-height: 1.5;
}
#examples-title {
    font-size: 1.3em;
    font-weight: 600;
    color: #e6e6fa;
    margin: 30px 0 15px 0;
    text-align: center;
}
footer {
    text-align: center;
    margin: 40px 0;
    padding: 20px;
    font-size: 1em;
    color: #d3d3fa;
}
footer a {
    color: #ff6b6b;
    text-decoration: none;
    font-weight: 500;
    transition: color 0.3s ease;
}
footer a:hover {
    color: #ff8e53;
}
.gr-plot {
    margin-top: 20px;
    background: rgba(255, 255, 255, 0.1);
    border-radius: 10px;
    padding: 15px;
    border: 1px solid rgba(255, 255, 255, 0.15);
}
.gr-examples .example {
    background: rgba(255, 255, 255, 0.12);
    border-radius: 8px;
    padding: 12px;
    margin: 8px 0;
    transition: all 0.3s ease;
    cursor: pointer;
}
.gr-examples .example:hover {
    background: rgba(255, 107, 107, 0.2);
    transform: translateY(-3px);
}
@keyframes fadeIn {
    from { opacity: 0; transform: translateY(20px); }
    to { opacity: 1; transform: translateY(0); }
}
.gr-panel, #title, #description, footer {
    animation: fadeIn 0.5s ease-out;
}
"""

# Gradio Blocks UI (Enhanced for vibrancy and UX)
with gr.Blocks(css=custom_css) as demo:
    # Header
    gr.Markdown(
        "<div id='title'>Emotion Analyzer BERT</div>",
        elem_id="title"
    )
    gr.Markdown(
        """
        <div id='description'>
        Discover the emotions in your text with our fine-tuned BERT model! 
        Type your thoughts below, adjust the confidence threshold, and explore the detected emotions with a vibrant visualization.
        </div>
        """,
        elem_id="description"
    )
    
    # Input Section
    with gr.Group():
        text_input = gr.Textbox(
            label="Share Your Thoughts",
            placeholder="Try something like 'I’m super excited today!' or 'This is so annoying...'",
            lines=3,
            show_label=True,
            elem_classes=["input-textbox"]
        )
        confidence_slider = gr.Slider(
            minimum=0.0,
            maximum=0.9,
            value=0.0,
            step=0.05,
            label="Confidence Threshold",
            info="Filter emotions below this confidence level (default thresholds apply)",
            elem_classes=["input-slider"]
        )
        submit_btn = gr.Button("Analyze Emotions", variant="primary")
    
    # Output Section
    with gr.Group():
        with gr.Row():
            with gr.Column(scale=1):
                processed_text_output = gr.Textbox(
                    label="Processed Text",
                    lines=1,
                    interactive=False,
                    elem_classes=["output-textbox"]
                )
                thresholded_output = gr.Textbox(
                    label="Detected Emotions (Above Threshold)",
                    lines=4,
                    interactive=False,
                    elem_classes=["output-textbox"]
                )
                top_5_output = gr.Textbox(
                    label="Top 5 Emotions",
                    lines=4,
                    interactive=False,
                    elem_classes=["output-textbox"]
                )
            with gr.Column(scale=1):
                output_plot = gr.Plot(
                    label="Emotion Confidence Visualization",
                    elem_classes=["output-plot"]
                )
    
    # Example carousel
    with gr.Group():
        gr.Markdown(
            "<div id='examples-title'>Explore Example Texts</div>",
            elem_id="examples-title"
        )
        examples = gr.Examples(
            examples=[
                ["I’m thrilled to win this award! 😄", "Joy Example"],
                ["This is so frustrating, nothing works. 😣", "Annoyance Example"],
                ["I feel so sorry for what happened. 😢", "Sadness Example"],
                ["What a beautiful day to be alive! 🌞", "Admiration Example"],
                ["Feeling nervous about the exam tomorrow 😓 u/student r/study", "Nervousness Example"]
            ],
            inputs=[text_input],
            label=""
        )
    
    # Footer
    gr.HTML(
        """
        <footer>
            Created by logasanjeev | 
            <a href="https://huggingface.co/logasanjeev/goemotions-bert">Model Card</a> | 
            <a href="https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-goemotions-bert/notebook">Kaggle Notebook</a>
        </footer>
        """
    )
    
    # Bind predictions
    submit_btn.click(
        fn=predict_emotions_with_details,
        inputs=[text_input, confidence_slider],
        outputs=[processed_text_output, thresholded_output, top_5_output, output_plot]
    )

# Launch
if __name__ == "__main__":
    demo.launch()