Create app.py

app.py

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+
+import gradio as gr
+import whisper
+from transformers import pipeline
+import librosa
+import soundfile as sf
+import tempfile
+
+model = whisper.load_model("base")
+
+sentiment_analysis = pipeline(
+  "sentiment-analysis",
+  framework="pt",
+  model="SamLowe/roberta-base-go_emotions"
+)
+
+def analyze_sentiment(text):
+  results = sentiment_analysis(text)
+  sentiment_results = {
+    result['label']: result['score'] for result in results
+  }
+  return sentiment_results
+
+def get_sentiment_emoji(sentiment):
+  # Define the mapping of sentiments to emojis
+  emoji_mapping = {
+    "disappointment": "😞",
+    "sadness": "😢",
+    "annoyance": "😠",
+    "neutral": "😐",
+    "disapproval": "👎",
+    "realization": "😮",
+    "nervousness": "😬",
+    "approval": "👍",
+    "joy": "😄",
+    "anger": "😡",
+    "embarrassment": "😳",
+    "caring": "🤗",
+    "remorse": "😔",
+    "disgust": "🤢",
+    "grief": "😥",
+    "confusion": "😕",
+    "relief": "😌",
+    "desire": "😍",
+    "admiration": "😌",
+    "optimism": "😊",
+    "fear": "😨",
+    "love": "❤️",
+    "excitement": "🎉",
+    "curiosity": "🤔",
+    "amusement": "😄",
+    "surprise": "😲",
+    "gratitude": "🙏",
+    "pride": "🦁"
+  }
+  return emoji_mapping.get(sentiment, "")
+
+def display_sentiment_results(sentiment_results, option):
+    sentiment_text = ""
+    for sentiment, score in sentiment_results.items():
+        emoji = get_sentiment_emoji(sentiment)
+        score_percentage = score * 100  # Corrected indentation
+        if option == "Sentiment Only":
+            sentiment_text += f"{sentiment} {emoji}\n"
+        elif option == "Sentiment + Score":
+            sentiment_text += f"{sentiment} {emoji}: {score_percentage:.2f}%\n"
+    return sentiment_text
+
+def load_and_resample_audio(file_path, target_sample_rate=16000):
+    audio, _ = librosa.load(file_path, sr=target_sample_rate)
+    temp_file_path = '/tmp/resampled_audio.wav'
+    sf.write(temp_file_path, audio, target_sample_rate)
+    return temp_file_path
+
+def inference(audio_file_path, sentiment_option):
+    resampled_audio_path = load_and_resample_audio(audio_file_path)
+    audio = whisper.load_audio(resampled_audio_path)
+    audio = whisper.pad_or_trim(audio)
+
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    _, probs = model.detect_language(mel)
+    lang = max(probs, key=probs.get)
+
+    options = whisper.DecodingOptions(fp16=False)
+    result = whisper.decode(model, mel, options)
+
+    sentiment_results = analyze_sentiment(result.text)
+    sentiment_output = display_sentiment_results(sentiment_results, sentiment_option)
+
+    return lang.upper(), result.text, sentiment_output
+
+title = "🎤 Gradio UI"
+description = "we have deployed our model on Gradio"
+
+block = gr.Blocks()
+
+with block:
+    gr.Markdown("# Mood Reader 🕵️‍♂️")
+    gr.Markdown("Your Words Whisper 🤫, But Emotions Shout 📢 – Discover What's Truly Behind Every Sentence with Mood Reader 🕵️‍♂️💬")
+    with gr.Column():
+        audio = gr.Audio(label="Input Audio", type="filepath")
+        sentiment_option = gr.Radio(choices=["Sentiment Only", "Sentiment + Score"], label="Select an option")
+        transcribe_btn = gr.Button("Transcribe")
+    lang_str = gr.Textbox(label="Language")
+    text = gr.Textbox(label="Transcription")
+    sentiment_output = gr.Textbox(label="Sentiment Analysis Results")
+
+    transcribe_btn.click(
+        inference,
+        inputs=[audio, sentiment_option],
+        outputs=[lang_str, text, sentiment_output]
+    )
+
+block.launch()