Create app.py

app.py

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+import gradio as gr
+import plotly.express as px
+import pandas as pd
+import logging
+import whisper
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import pandas as pd
+from torch.nn.functional import silu
+from torch.nn.functional import softplus
+from einops import rearrange, repeat, einsum
+from transformers import AutoTokenizer, AutoModel
+from torch import Tensor
+from einops import rearrange
+
+from model import Mamba
+
+logging.basicConfig(level=logging.INFO)
+
+def plotly_plot_text(text):
+    data = pd.DataFrame()
+    data['Emotion'] = ['😠 anger', '🤢 disgust', '😨 fear', '😄 joy/happiness', '😐 neutral', '😢 sadness', '😲 surprise/enthusiasm']
+    data['Probability'] = model.predict_proba([text])[0].tolist()
+    p = px.bar(data, x='Emotion', y='Probability', color="Probability")
+    return (
+            p,
+            f"🗣️ Transcription:\n{text}",
+            f"## 🏆 Dominant Emotion: {data['Emotion'].values[np.argmax(np.array(data['Probability']))]}"
+        )
+
+def transcribe_audio(audio_path):
+    whisper_model = whisper.load_model("base")
+    try:
+        result = whisper_model.transcribe(audio_path, fp16=False)
+        return result.get('text', '')
+    except Exception as e:
+        logging.error(f"Transcription failed: {e}")
+        return ""
+
+def plotly_plot_audio(audio_path):
+    data = pd.DataFrame()
+    data['Emotion'] = ['😠 anger', '🤢 disgust', '😨 fear', '😄 joy/happiness', '😐 neutral', '😢 sadness', '😲 surprise/enthusiasm']
+    try:
+        text = transcribe_audio(audio_path)
+        data['Probability'] = model.predict_proba([text])[0].tolist() if text.strip() else [0.0] * data.shape[0]
+        p = px.bar(data, x='Emotion', y='Probability', color="Probability")
+        return (
+            p,
+            f"🗣️ Transcription:\n{text}",
+            f"## 🏆 Dominant Emotion: {data['Emotion'].values[np.argmax(np.array(data['Probability']))]}"
+        )
+
+    except Exception as e:
+        logging.error(f"Processing failed: {e}")
+        data['Probability'] = [0] * data.shape[0]
+        p = px.bar(data, x='Emotion', y='Probability', color="Probability")
+        return (
+            p,
+            "❌ Error processing audio",
+            "⚠️ Processing Error"
+        )
+    
+def create_demo():
+    with gr.Blocks(theme=gr.themes.Soft(), title="Emotion Detection") as demo:
+        gr.Markdown("# Text-based bilingual emotion recognition")
+
+        with gr.Row():
+            with gr.Column():
+                audio_input = gr.Audio(
+                    sources=["upload", "microphone"],
+                    type="filepath",
+                    label="Record or Upload Audio",
+                    format="wav",
+                    interactive=True
+                )
+            with gr.Column():
+                text_input = gr.Text(label="Write Text")
+
+        with gr.Row():
+            top_emotion = gr.Markdown("## 🏆 Dominant Emotion: Waiting for input ...",
+                                      elem_classes="dominant-emotion")
+
+        with gr.Row():
+            text_plot = gr.Plot(label="Text Analysis")
+
+        transcription = gr.Textbox(
+            label="📜 Transcription Results",
+            placeholder="Transcribed text will appear here...",
+            lines=3,
+            max_lines=6
+        )
+
+        if text_input is not None:
+            text_input.change(fn=plotly_plot_text, inputs=text_input, outputs=[text_plot, transcription, top_emotion])
+        elif audio_input is not None:
+            audio_input.change(fn=plotly_plot_audio, inputs=audio_input, outputs=[text_plot, transcription, top_emotion])
+    return demo
+
+
+if __name__ == "__main__":
+    model = Mamba(num_layers = 2, d_input = 1024, d_model = 512, num_classes=7, model_name='jina', pooling=None).to(device)
+    checkpoint = torch.load("Mamba_jina_checkpoint.pth"), map_location=torch.device('cpu')
+    model.load_state_dict(checkpoint['model_state_dict'])
+    demo = create_demo()
+    demo.launch()