app.py

# imports
import gradio as gr
import os
import requests
from transformers import pipeline

# Set your FastAPI backend endpoint
BACKEND_URL = "https://asr-evaluation-backend.emergentai.ug/submit-feedback"

# Language-to-model map
model_map = {
    "hausa": "asr-africa/wav2vec2-xls-r-1b-naijavoices-hausa-500hr-v0",
    "igbo": "asr-africa/wav2vec2-xls-r-1b-naijavoices-igbo-500hr-v0",
    "yoruba": "asr-africa/wav2vec2-xls-r-1b-naijavoices-yoruba-500hr-v0",
    "zulu": "asr-africa/W2V2-Bert_nchlt_speech_corpus_Fleurs_ZULU_63hr_v1",
    "xhosa": "asr-africa/wav2vec2_xls_r_300m_nchlt_speech_corpus_Fleurs_XHOSA_63hr_v1",
    "afrikaans": "asr-africa/mms-1B_all_nchlt_speech_corpus_Fleurs_CV_AFRIKAANS_57hr_v1",
    "bemba": "asr-africa/whisper_BIG-C_BEMBA_189hr_v1",
    "shona": "asr-africa/W2V2_Bert_Afrivoice_FLEURS_Shona_100hr_v1",
    "luganda": "asr-africa/whisper-small-CV-Fleurs-lg-313hrs-v1",
    "swahili": "asr-africa/wav2vec2-xls-r-300m-CV_Fleurs_AMMI_ALFFA-sw-400hrs-v1",
    "lingala": "asr-africa/wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-109hrs-v2",
    "amharic": "asr-africa/facebook-mms-1b-all-common_voice_fleurs-amh-200hrs-v1",
    "kinyarwanda": "asr-africa/facebook-mms-1b-all-common_voice_fleurs-rw-100hrs-v1",
    "oromo": "asr-africa/mms-1b-all-Sagalee-orm-85hrs-4",
    "akan": "asr-africa/wav2vec2-xls-r-akan-100-hours",
    "ewe": "asr-africa/wav2vec2-xls-r-ewe-100-hours",
    "wolof": "asr-africa/w2v2-bert-Wolof-20-hours-Google-Fleurs-ALF-dataset",
    "bambara": "asr-africa/mms-bambara-50-hours-mixed-bambara-dataset",
}

# Create storage directory
os.makedirs("responses", exist_ok=True)

# Transcription function
def transcribe(audio, language):
    asr = pipeline("automatic-speech-recognition", model=model_map[language], device=0)
    text = asr(audio)["text"]
    return text, audio

# Save feedback by sending it to FastAPI backend
def save_feedback(audio_file, transcription, lang, age_group, gender, speak_level, write_level,
                  native, native_language, education_level, multilingual, other_languages,
                  regional_accent, accent_desc, env, device, domain, accuracy, orthography, orthography_issues,
                  meaning, meaning_loss, errors, error_examples, performance, improvement,
                  usability, technical_issues_bool, technical_issues_desc, final_comments, email):

    try:
        with open(audio_file, "rb") as f:
            audio_content = f.read()

        metadata = {
            "transcription": transcription,
            "age_group": age_group,
            "gender": gender,
            "evaluated_language": lang,
            "speak_level": speak_level,
            "write_level": write_level,
            "native": native,
            "native_language": native_language,
            "education_level": education_level,
            "multilingual": multilingual,
            "other_languages": other_languages,
            "regional_accent": regional_accent,
            "accent_description": accent_desc,
            "environment": env,
            "device": device,
            "domain": domain,
            "accuracy": accuracy,
            "orthography": orthography,
            "orthography_issues": orthography_issues,
            "meaning": meaning,
            "meaning_loss": meaning_loss,
            "errors": ",".join(errors) if errors else "",
            "error_examples": error_examples,
            "performance": performance,
            "improvement": improvement,
            "usability": usability,
            "technical_issues": technical_issues_bool,
            "technical_issues_desc": technical_issues_desc,
            "final_comments": final_comments,
            "email": email
        }

        files = {
            "audio_file": ("audio.wav", audio_content, "audio/wav")
        }

        response = requests.post(BACKEND_URL, data=metadata, files=files, timeout=20)

        if response.status_code == 201:
            return "✅ Feedback submitted successfully. Thank you!"
        else:
            return f"⚠️ Submission failed: {response.status_code} — {response.text}"

    except Exception as e:
        return f"❌ Could not connect to the backend: {str(e)}"

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## African ASR + Feedback")

    with gr.Row():
        audio_input = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Upload or record audio")
        lang = gr.Dropdown(list(model_map.keys()), label="Select Language")

    transcribed_text = gr.Textbox(label="Transcribed Text")
    submit_btn = gr.Button("Transcribe")
    submit_btn.click(fn=transcribe, inputs=[audio_input, lang], outputs=[transcribed_text, audio_input])

    gr.Markdown("---\n## Feedback Form")

    age_group = gr.Dropdown(["18 to 30", "31 to 50", "50+", "Prefer not to say"], label="Age Group *")
    gender = gr.Dropdown(["Male", "Female", "Prefer not to say"], label="Gender *")
    speak_level = gr.Slider(1, 10, step=1, label="How well do you speak this language? *")
    write_level = gr.Slider(1, 10, step=1, label="How well do you write the language? *")
    native = gr.Radio(["Yes", "No"], label="Are you a native speaker of this language? *")
    native_language = gr.Textbox(label="If you are not a native speaker, what is your native language?")

    # ✅ NEW: Education level
    education_level = gr.Dropdown(["Primary", "Secondary", "Tertiary", "None", "Prefer not to say"], label="What is your highest level of education? *")

    # ✅ NEW: Multilingual + gated text input
    multilingual = gr.Radio(["Yes", "No"], label="Are you multilingual (i.e., speak more than one language)? *")
    other_languages = gr.Textbox(label="What other languages do you speak?")
    multilingual.change(fn=lambda x: gr.update(visible=x == "Yes"), inputs=multilingual, outputs=other_languages)

    # ✅ NEW: Regional Accent + gated text input
    regional_accent = gr.Radio(["Yes", "No", "Unsure"], label="Did the speaker in the audio have a regional accent? *")
    accent_desc = gr.Textbox(label="If yes, please describe the accent or region.")
    regional_accent.change(fn=lambda x: gr.update(visible=x == "Yes"), inputs=regional_accent, outputs=accent_desc)

    env = gr.Dropdown(["Studio/Professional Recording", "Quiet Room (minimal noise)", "Noisy Background (e.g., street, cafe, market)", "Multiple Environments", "Unsure"], label="What was the type of recording environment for the speech you evaluated? *")
    device = gr.Dropdown(["Mobile Phone/Tablet", "Tablet", "Laptop/Computer Microphone", "Dedicated Microphone (e.g., headset, studio mic)", "Unsure"], label="What type of recording device was used? *")
    domain = gr.Textbox(label="If yes, please specify the domain/topic (e.g., news broadcast, casual conversation, lecture, medical, parliamentary, religious).")
    accuracy = gr.Slider(1, 10, step=1, label="Overall, how accurate was the model's transcription for the audio you reviewed? *")
    orthography = gr.Radio(["Yes, mostly correct", "No, major issues", "Partially (some correct, some incorrect)", "Not Applicable / Unsure"], label="Did the transcription correctly use the standard orthography?")
    orthography_issues = gr.Textbox(label="If you selected 'No' or 'Partially', please describe the issues.")
    meaning = gr.Slider(1, 5, step=1, label="Did the model's transcription preserve the original meaning of the speech? *")
    meaning_loss = gr.Textbox(label="If the meaning was not fully preserved, please explain how.")
    errors = gr.CheckboxGroup([
        "Substitutions (wrong words used)",
        "Omissions (words missing)",
        "Insertions (extra words added)",
        "Pronunciation-related errors (phonetically plausible but wrong word/spelling)",
        "Diacritic/Tone/Special Character errors",
        "Code-switching errors (mixing languages incorrectly)",
        "Named Entity errors (names of people/places wrong)",
        "Punctuation errors",
        "No significant errors observed"
    ], label="Which types of errors were most prominent or impactful in the transcriptions? *")
    error_examples = gr.Textbox(label="(Optional) Can you provide 1–2 examples of significant errors and how you would correct them?")
    performance = gr.Textbox(label="Please describe the model's performance in your own words. What did it do well? What did it struggle with? *")
    improvement = gr.Textbox(label="How could this ASR model be improved? What features would be most helpful? *")
    usability = gr.Slider(1, 5, step=1, label="How easy was it to use the Hugging Face evaluation tool/interface? *")
    technical_issues_bool = gr.Radio(["Yes", "No"], label="Did you encounter any technical issues using the tool? *")
    technical_issues_desc = gr.Textbox(label="If yes, please describe the technical issues you encountered.")
    final_comments = gr.Textbox(label="Any other comments or suggestions regarding the evaluation process or ASR model?")
    email = gr.Textbox(label="Email")

    save_btn = gr.Button("Submit Feedback")
    output_msg = gr.Textbox(interactive=False)

    save_btn.click(
        fn=save_feedback,
        inputs=[
            audio_input, transcribed_text, lang, age_group, gender, speak_level, write_level,
            native, native_language, education_level, multilingual, other_languages,
            regional_accent, accent_desc, env, device, domain, accuracy, orthography, orthography_issues,
            meaning, meaning_loss, errors, error_examples, performance, improvement,
            usability, technical_issues_bool, technical_issues_desc, final_comments, email
        ],
        outputs=[output_msg]
    )

# Launch
demo.launch()