app.py

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

# Load inference.py and model
repo_id = "logasanjeev/goemotions-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 (simplified, no export)
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 top 5 (though not displayed in this simplified version)
    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]
    
    # 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 bar chart
    fig = None
    if filtered_predictions:
        df = pd.DataFrame(filtered_predictions, columns=["Emotion", "Confidence"])
        fig = px.bar(
            df,
            x="Emotion",
            y="Confidence",
            color="Emotion",
            text="Confidence",
            title="Emotion Confidence Levels",
            height=300,
            color_discrete_sequence=px.colors.qualitative.Pastel
        )
        fig.update_traces(texttemplate='%{text:.2f}', textposition='auto')
        fig.update_layout(showlegend=False, margin=dict(t=40, b=40), xaxis_title="", yaxis_title="Confidence")
    
    return processed_text, thresholded_output, fig

# Simplified CSS
custom_css = """
body {
    font-family: 'Arial', sans-serif;
    background-color: #f9f9f9;
}
.gr-panel {
    border-radius: 8px;
    box-shadow: 0 2px 10px rgba(0,0,0,0.05);
    background: white;
    padding: 15px;
    margin-bottom: 15px;
}
.gr-button {
    border-radius: 6px;
    padding: 10px 20px;
    font-weight: 500;
    background: #4a90e2;
    color: white;
    transition: background 0.3s ease;
}
.gr-button:hover {
    background: #357abd;
}
#title {
    font-size: 2.2em;
    font-weight: 600;
    color: #333;
    text-align: center;
    margin-bottom: 10px;
}
#description {
    font-size: 1.1em;
    color: #666;
    text-align: center;
    max-width: 600px;
    margin: 0 auto 20px auto;
}
#examples-title {
    font-size: 1.3em;
    font-weight: 500;
    color: #333;
    margin-bottom: 10px;
}
footer {
    text-align: center;
    margin-top: 30px;
    padding: 15px;
    font-size: 0.9em;
    color: #666;
}
footer a {
    color: #4a90e2;
    text-decoration: none;
}
footer a:hover {
    text-decoration: underline;
}
"""

# Gradio Blocks UI (Simplified)
with gr.Blocks(css=custom_css) as demo:
    # Header
    gr.Markdown("<div id='title'>GoEmotions BERT Classifier</div>", elem_id="title")
    gr.Markdown(
        """
        <div id='description'>
        Predict emotions from text using a fine-tuned BERT model. 
        Enter your text below to see the detected emotions and their confidence scores.
        </div>
        """,
        elem_id="description"
    )
    
    # Input Section
    with gr.Group():
        text_input = gr.Textbox(
            label="Enter Your Text",
            placeholder="Type something like 'I’m just chilling today'...",
            lines=2,
            show_label=False
        )
        confidence_slider = gr.Slider(
            minimum=0.0,
            maximum=0.9,
            value=0.0,
            step=0.05,
            label="Minimum Confidence Threshold",
            info="Filter predictions below this confidence level (default thresholds still apply)"
        )
        submit_btn = gr.Button("Predict Emotions")
    
    # Output Section
    with gr.Group():
        processed_text_output = gr.Textbox(label="Preprocessed Text", lines=1, interactive=False)
        thresholded_output = gr.Textbox(label="Predicted Emotions", lines=3, interactive=False)
        output_plot = gr.Plot(label="Emotion Confidence Chart")
    
    # Example carousel
    with gr.Group():
        gr.Markdown("<div id='examples-title'>Try These Examples</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>
            Built 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, output_plot]
    )

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