Create app.py

app.py

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+# voicepulse.py (multilingual feedback transcriber and word cloud generator with export)
+
+import gradio as gr
+import numpy as np
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
+import nltk
+import io
+import torch
+import csv
+from gtts import gTTS
+
+nltk.download("stopwords")
+stop_words = set(nltk.corpus.stopwords.words("english"))
+
+# Translation model for multilingual -> English
+translation_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+translation_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+device = 0 if torch.cuda.is_available() else -1
+
+# In-memory feedback word list and archive
+feedback_words = []
+all_feedback = []
+
+# Sample audios
+sample_texts = {
+    "Telugu Sample": "మీ సేవలు చాలా బాగున్నాయి. మేము చాలా సంతృప్తిగా ఉన్నాము.",
+    "Hindi Sample": "आपकी सेवा बहुत अच्छी थी और हम संतुष्ट हैं।",
+    "Tamil Sample": "உங்கள் சேவை மிகவும் சிறந்ததாக இருந்தது. நாங்கள் திருப்தி அடைந்தோம்.",
+    "English Sample": "Your support team was helpful and responsive."
+}
+
+def generate_sample_audio(text, lang_code):
+    tts = gTTS(text, lang=lang_code)
+    tts.save("sample_full.mp3")
+
+    from pydub import AudioSegment
+    full_audio = AudioSegment.from_mp3("sample_full.mp3")
+    short_audio = full_audio[:3000]  # first 3 seconds
+    short_audio.export("sample.mp3", format="mp3")
+    return "sample.mp3"
+
+def translate(text, src_lang, tgt_lang="eng_Latn"):
+    translation_pipeline = pipeline(
+        "translation",
+        model=translation_model,
+        tokenizer=translation_tokenizer,
+        src_lang=src_lang,
+        tgt_lang=tgt_lang,
+        max_length=400,
+        device=device
+    )
+    result = translation_pipeline(text)
+    return result[0]['translation_text']
+
+def get_transcription(audio, language):
+    sr, y = audio
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    if language == "English":
+        transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+        return transcriber({"sampling_rate": sr, "raw": y})["text"]
+
+    model_map = {
+        "Hindi": ("theainerd/Wav2Vec2-large-xlsr-hindi", "hin_Deva"),
+        "Telugu": ("anuragshas/wav2vec2-large-xlsr-53-telugu", "tel_Telu"),
+        "Tamil": ("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250", "tam_Taml"),
+        "Kannada": ("vasista22/whisper-kannada-medium", "kan_Knda")
+    }
+
+    model_name, src_lang = model_map[language]
+    transcriber = pipeline("automatic-speech-recognition", model=model_name)
+    text = transcriber({"sampling_rate": sr, "raw": y})["text"]
+    return translate(text, src_lang)
+
+def process_feedback(audio, language):
+    transcription = get_transcription(audio, language)
+
+    # Use summarization to extract core feedback idea
+    summarizer = pipeline("summarization", model="mrm8488/distilbart2cnn-12-6")
+    summary = summarizer(transcription, max_length=60, min_length=10, do_sample=False)[0]['summary_text']
+
+    # Save for download
+    all_feedback.append({"Language": language, "Transcription": transcription, "Summary": summary})
+
+    # Extract meaningful words from summary
+    words = [w for w in summary.lower().split() if w.isalpha() and w not in stop_words]
+    feedback_words.extend(words)
+
+    freq = {w: feedback_words.count(w) for w in set(feedback_words)}
+    wc = WordCloud(width=800, height=400, background_color="white").generate_from_frequencies(freq)
+    buf = io.BytesIO()
+    plt.imshow(wc, interpolation="bilinear")
+    plt.axis("off")
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    image = plt.imread(buf, format="png")
+
+    return summary, image
+
+def export_to_csv():
+    with open("feedback_export.csv", "w", newline="") as csvfile:
+        fieldnames = ["Language", "Transcription", "Summary"]
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+        for row in all_feedback:
+            writer.writerow(row)
+    return "feedback_export.csv"
+
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# 🎙️ VoicePulse Multilingual Feedback Collector")
+    gr.Markdown("""
+    🗣️ **VoicePulse** lets you speak feedback in your language — Telugu, Hindi, Tamil, Kannada, or English.
+    It transcribes, translates, and summarizes the feedback, building a live word cloud to show what people care about.
+    Try speaking or use sample audio buttons below!
+    """)
+
+    with gr.Row():
+        audio_input = gr.Audio(type="numpy", label="🎤 Speak your feedback")
+        lang_dropdown = gr.Dropdown(label="🌐 Language", choices=["English", "Hindi", "Telugu", "Tamil", "Kannada"], value="English")
+
+    with gr.Row():
+        submit_btn = gr.Button("Process Feedback")
+        
+    with gr.Row():
+        gr.Markdown("### 🎧 Sample Feedback (Telugu, Hindi, Tamil, English)")
+        sample_btn_te = gr.Button("🔉 Telugu Sample")
+        sample_btn_hi = gr.Button("🔉 Hindi Sample")
+        sample_btn_ta = gr.Button("🔉 Tamil Sample")
+        sample_btn_en = gr.Button("🔉 English Sample")
+        sample_audio = gr.Audio(label="🔊 Sample Audio Output (Preview)")
+        
+    with gr.Row():
+        summary_out = gr.Textbox(label="📝 Summarized Feedback")
+        wordcloud_out = gr.Image(type="pil", label="☁️ Word Cloud of All Feedback")
+
+    with gr.Row():
+        export_btn = gr.Button("📁 Export Feedback to CSV")
+        csv_file_output = gr.File(label="📄 Download CSV")
+
+    submit_btn.click(process_feedback, inputs=[audio_input, lang_dropdown], outputs=[summary_out, wordcloud_out])
+    export_btn.click(export_to_csv, inputs=[], outputs=csv_file_output)
+    sample_btn_te.click(lambda: generate_sample_audio(sample_texts["Telugu Sample"], 'te'), inputs=[], outputs=sample_audio)
+    sample_btn_hi.click(lambda: generate_sample_audio(sample_texts["Hindi Sample"], 'hi'), inputs=[], outputs=sample_audio)
+    sample_btn_ta.click(lambda: generate_sample_audio(sample_texts["Tamil Sample"], 'ta'), inputs=[], outputs=sample_audio)
+    sample_btn_en.click(lambda: generate_sample_audio(sample_texts["English Sample"], 'en'), inputs=[], outputs=sample_audio)
+
+demo.launch()