Create app.py

app.py

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+# 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"
+
+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, env, device, domain, accuracy, orthography, orthography_issues,
+                  meaning, meaning_loss, errors, error_examples, performance):
+
+    try:
+        with open(audio_file, "rb") as f:
+            audio_content = f.read()
+
+        metadata = {
+            "transcription": transcription,
+            "evaluated_language": lang,
+            "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
+        }
+
+        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 Evaluation Platform")
+
+    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")
+    user_id = gr.Textbox(label="Please enter user ID.")
+    env = gr.Dropdown(["Studio/Professional Recording", "Quiet Room (minimal noise)", "Noisy Background (e.g., street, cafe, market)","Other"], label="What was the type of recording environment for the speech you evaluated? *")
+    device = gr.Dropdown(["Mobile Phone/Tablet", "Laptop/Computer Microphone", "Dedicated Microphone (e.g., headset, studio mic)", "Other"], label="What type of recording device  was used? *")
+    domain = gr.Textbox(label="Was the speech related to a specific topic? If yes, please specify the topic (e.g., news, education, medical, law, religious, sports, science).")
+    accuracy = gr.Slider(1, 10, step=1, label="Overall, how accurate was the model's transcription for the audio you reviewed? *")
+    transcript_edit = gr.Textbox(label="If the transcription provided by the model was incorrect, please enter your corrected version.")
+    orthography = gr.Radio(["Yes, mostly correct", "No, major issues", "Partially (some correct, some incorrect)", "Not Applicable"], label="Did the transcription correctly use the standard orthography (including accents, diacritics, special characters) for the language?")
+    orthography_issues = gr.Textbox(label="If you selected \"No\" or \"Partially\", please describe any significant orthography issues you noticed.")
+    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 (i.e., you rated 1-4 above), please briefly explain how it was changed or lost.")
+    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? *")
+
+    save_btn = gr.Button("Submit Feedback")
+    output_msg = gr.Textbox(interactive=False)
+    save_btn.click(
+    fn=save_feedback,
+    inputs=[lang, native_language, env, device, domain, accuracy, orthography, orthography_issues,
+            meaning, meaning_loss, errors, error_examples, performance],
+    outputs=[output_msg]
+    )
+
+# Launch the interface
+demo.launch()