import gradio as gr
from transformers import pipeline
import pandas as pd
import plotly.express as px

# ------------------------------
# Load pretrained models
# ------------------------------
text_classifier = pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    return_all_scores=True
)

audio_classifier = pipeline(
    "audio-classification",
    model="superb/wav2vec2-base-superb-er"
)

# ------------------------------
# Map emotion to emoji
# ------------------------------
EMOJI_MAP = {
    "anger": "😡",
    "disgust": "🤢",
    "fear": "😨",
    "joy": "😄",
    "neutral": "😐",
    "sadness": "😢",
    "surprise": "😲",
    "hap": "😄",  # for audio model
    "neu": "😐",
    "sad": "😢",
    "ang": "😡"
}

# ------------------------------
# Fusion function
# ------------------------------
def fuse_predictions(text_preds=None, audio_preds=None, w_text=0.5, w_audio=0.5):
    labels = set()
    if text_preds:
        labels |= {p['label'] for p in text_preds}
    if audio_preds:
        labels |= {p['label'] for p in audio_preds}
    scores = {l: 0.0 for l in labels}

    def normalize(preds):
        s = sum(p['score'] for p in preds)
        return {p['label']: p['score']/s for p in preds}

    if text_preds:
        t_norm = normalize(text_preds)
        for l in labels:
            scores[l] += w_text * t_norm.get(l, 0)
    if audio_preds:
        a_norm = normalize(audio_preds)
        for l in labels:
            scores[l] += w_audio * a_norm.get(l, 0)

    best = max(scores.items(), key=lambda x: x[1]) if scores else ("none", 0)
    return {"fused_label": best[0], "fused_score": round(best[1], 3), "all_scores": scores}

# ------------------------------
# Create bar chart
# ------------------------------
def make_bar_chart(scores_dict, title="Emotion Scores"):
    df = pd.DataFrame({
        "Emotion": list(scores_dict.keys()),
        "Score": list(scores_dict.values())
    })
    fig = px.bar(df, x="Emotion", y="Score", text="Score",
                 title=title, range_y=[0,1],
                 color="Emotion", color_discrete_sequence=px.colors.qualitative.Bold)
    fig.update_traces(texttemplate='%{text:.2f}', textposition='outside')
    fig.update_layout(yaxis_title="Probability", xaxis_title="Emotion", showlegend=False)
    return fig

# ------------------------------
# Prediction function
# ------------------------------
def predict(text, audio, w_text, w_audio):
    text_preds, audio_preds = None, None
    if text:
        text_preds = text_classifier(text)[0]
    if audio:
        audio_preds = audio_classifier(audio)
    fused = fuse_predictions(text_preds, audio_preds, w_text, w_audio)

    # Display final predicted emotion with emoji
    label = fused['fused_label']
    emoji = EMOJI_MAP.get(label, "")
    final_emotion = f"### Final Predicted Emotion: {label.upper()} {emoji} (score: {fused['fused_score']})"

    # Bar charts
    charts = []
    if text_preds:
        charts.append(make_bar_chart({p['label']: p['score'] for p in text_preds}, "Text Emotion Scores"))
    if audio_preds:
        charts.append(make_bar_chart({p['label']: p['score'] for p in audio_preds}, "Audio Emotion Scores"))
    charts.append(make_bar_chart(fused['all_scores'], "Fused Emotion Scores"))

    return final_emotion, charts

# ------------------------------
# Build Gradio interface
# ------------------------------
with gr.Blocks() as demo:
    gr.Markdown("## 🎭 Multimodal Emotion Classification (Text + Speech)")

    with gr.Row():
        with gr.Column():
            txt = gr.Textbox(label="Text input", placeholder="Type something emotional...")
            aud = gr.Audio(type="filepath", label="Upload speech (wav/mp3)")
            w1 = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Text weight (w_text)")
            w2 = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Audio weight (w_audio)")
            btn = gr.Button("Predict")
        with gr.Column():
            final_label = gr.Markdown(label="Predicted Emotion")
            chart_output = gr.Plot(label="Emotion Scores")

    # Button click triggers prediction
    btn.click(fn=predict, inputs=[txt, aud, w1, w2], outputs=[final_label, chart_output])

demo.launch()