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| import streamlit as st | |
| import whisper | |
| import torch | |
| import spacy | |
| from transformers import pipeline | |
| # Load spaCy Model | |
| try: | |
| nlp = spacy.load("en_core_web_sm") | |
| except: | |
| import subprocess | |
| subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"]) | |
| nlp = spacy.load("en_core_web_sm") | |
| # Load Hugging Face Models | |
| def load_models(): | |
| sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| return sentiment_analyzer, summarizer | |
| sentiment_analyzer, summarizer = load_models() | |
| # Load Whisper Model | |
| def load_whisper(): | |
| return whisper.load_model("base") | |
| whisper_model = load_whisper() | |
| # Streamlit UI | |
| st.title("🎙️ Voice-Controlled AI Text Editor") | |
| st.subheader("Supports Speech-to-Text, Sentiment Analysis, Summarization & POS Tagging") | |
| # File Upload for Whisper | |
| uploaded_audio = st.file_uploader("🎵 Upload an audio file", type=["wav", "mp3", "m4a"]) | |
| if uploaded_audio: | |
| st.audio(uploaded_audio, format="audio/wav") | |
| with open("temp_audio.wav", "wb") as f: | |
| f.write(uploaded_audio.read()) | |
| with st.spinner("Transcribing..."): | |
| result = whisper_model.transcribe("temp_audio.wav") | |
| text = result["text"] | |
| st.success("Transcription Complete!") | |
| st.text_area("Transcribed Text", text, height=200) | |
| # Function to process voice commands | |
| def process_command(command, text): | |
| command = command.lower() | |
| if "summarize" in command: | |
| return summarize_text(text) | |
| elif "analyze sentiment" in command: | |
| return analyze_sentiment(text) | |
| elif "delete" in command: | |
| return "" | |
| else: | |
| return text # Return original text if no command is matched | |
| # Summarization Function | |
| def summarize_text(text): | |
| if len(text.split()) < 30: | |
| return "Text is too short for summarization." | |
| summary = summarizer(text[:1024], max_length=100, min_length=30, do_sample=False) | |
| return summary[0]['summary_text'] | |
| # Sentiment Analysis Function | |
| def analyze_sentiment(text): | |
| result = sentiment_analyzer(text[:512]) | |
| return f"Sentiment: {result[0]['label']} (Confidence: {result[0]['score']:.2f})" | |
| # POS Tagging Function | |
| def pos_tagging(text): | |
| doc = nlp(text) | |
| return [f"{token.text} -> {token.pos_}" for token in doc] | |
| # Voice Command for Summarization or Sentiment Analysis | |
| if st.button("🎙️ Speak Command"): | |
| with st.spinner("Listening..."): | |
| result = whisper_model.transcribe("temp_audio.wav") | |
| command = result["text"] | |
| st.write(f"Command Recognized: {command}") | |
| # Process the command | |
| processed_text = process_command(command, text) | |
| st.text_area("Processed Text", processed_text, height=200) | |
| # Sidebar Options | |
| with st.sidebar: | |
| st.header("⚡ Actions") | |
| if st.button("😊 Analyze Sentiment"): | |
| sentiment = analyze_sentiment(text) | |
| st.success(sentiment) | |
| if st.button("📝 Summarize Text"): | |
| summary = summarize_text(text) | |
| st.success(summary) | |
| if st.button("🔍 Show POS Tags"): | |
| pos_tags = pos_tagging(text) | |
| st.write("🔎 POS Tags:", pos_tags) | |
| if st.button("❌ Clear Text"): | |
| text = "" | |
| st.success("Text cleared.") | |