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| import gradio as gr | |
| import numpy as np | |
| from textblob import TextBlob | |
| import speech_recognition as sr | |
| class SentimentAnalyzer: | |
| def __init__(self): | |
| self.recognizer = sr.Recognizer() | |
| def audio_to_text(self, audio): | |
| """Convert audio to text using speech recognition""" | |
| try: | |
| # Get audio data from Gradio input | |
| sample_rate, audio_data = audio | |
| # Convert audio data to audio file format that speech_recognition can use | |
| import io | |
| import scipy.io.wavfile as wav | |
| byte_io = io.BytesIO() | |
| wav.write(byte_io, sample_rate, audio_data.astype(np.int16)) | |
| byte_io.seek(0) | |
| # Use speech recognition | |
| with sr.AudioFile(byte_io) as source: | |
| audio_data = self.recognizer.record(source) | |
| text = self.recognizer.recognize_google(audio_data) | |
| return text | |
| except Exception as e: | |
| return f"Error in speech recognition: {str(e)}" | |
| def analyze_sentiment(self, text): | |
| """Analyze sentiment using TextBlob""" | |
| try: | |
| blob = TextBlob(text) | |
| # Get polarity (-1 to 1) and subjectivity (0 to 1) | |
| polarity = blob.sentiment.polarity | |
| subjectivity = blob.sentiment.subjectivity | |
| # Determine sentiment category | |
| if polarity > 0: | |
| sentiment = "Positive" | |
| elif polarity < 0: | |
| sentiment = "Negative" | |
| else: | |
| sentiment = "Neutral" | |
| # Format results | |
| results_text = f""" | |
| Detected Text: "{text}" | |
| Analysis Results: | |
| - Overall Sentiment: {sentiment} | |
| - Polarity Score: {polarity:.2f} (-1 to +1) | |
| - Subjectivity Score: {subjectivity:.2f} (0 to 1) | |
| """ | |
| # Prepare plot data | |
| plot_data = { | |
| "labels": ["Polarity", "Subjectivity"], | |
| "values": [polarity * 100, subjectivity * 100] # Convert to percentage for visualization | |
| } | |
| return results_text, plot_data | |
| except Exception as e: | |
| return f"Error in sentiment analysis: {str(e)}", None | |
| def create_interface(): | |
| analyzer = SentimentAnalyzer() | |
| def process_audio(audio): | |
| if audio is None: | |
| return "Please provide an audio input.", None | |
| # Convert audio to text | |
| text = analyzer.audio_to_text(audio) | |
| if text.startswith("Error"): | |
| return text, None | |
| # Analyze sentiment | |
| return analyzer.analyze_sentiment(text) | |
| # Create Gradio interface | |
| with gr.Blocks() as interface: | |
| gr.Markdown("# 🎤 Speech Sentiment Analysis") | |
| gr.Markdown(""" | |
| Speak or upload an audio file to analyze its emotional content. | |
| The system will convert speech to text and analyze the sentiment. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_input = gr.Audio( | |
| label="Upload or Record Audio", | |
| type="numpy", | |
| sources=["microphone", "upload"] | |
| ) | |
| analyze_btn = gr.Button("Analyze Sentiment") | |
| with gr.Column(): | |
| output_text = gr.Textbox( | |
| label="Analysis Results", | |
| lines=8 | |
| ) | |
| output_plot = gr.BarPlot( | |
| title="Sentiment Scores", | |
| x_title="Metrics", | |
| y_title="Score (%)" | |
| ) | |
| analyze_btn.click( | |
| fn=process_audio, | |
| inputs=[audio_input], | |
| outputs=[output_text, output_plot] | |
| ) | |
| gr.Markdown(""" | |
| ### How to Use: | |
| 1. Click the microphone button to record or upload an audio file | |
| 2. Click "Analyze Sentiment" to process | |
| 3. View the results showing: | |
| - Detected text from speech | |
| - Overall sentiment (Positive/Negative/Neutral) | |
| - Polarity score (-100% to +100%) | |
| - Subjectivity score (0% to 100%) | |
| """) | |
| return interface | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch(share=True) |