Create app.py

app.py

@@ -0,0 +1,108 @@
+import gradio as gr
+import os
+import requests
+from transformers import pipeline
+
+# Set your FastAPI backend endpoint
+BACKEND_URL = "https://09fc-41-84-202-90.ngrok-free.app/submit-feedback"
+
+# Map of models
+model_map = {
+    "english": "jonatasgrosman/wav2vec2-large-xlsr-53-english"
+}
+
+# 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, age_group, gender, evaluated_language, speak_level, write_level,
+                  native, native_language, env, device, domain, accuracy, orthography, meaning, errors,
+                  performance, improvement, usability, technical_issues, final_comments, email):
+    data = {
+        "transcription": transcription,
+        "age_group": age_group,
+        "gender": gender,
+        "evaluated_language": evaluated_language,
+        "speak_level": speak_level,
+        "write_level": write_level,
+        "native": native,
+        "native_language": native_language,
+        "environment": env,
+        "device": device,
+        "domain": domain,
+        "accuracy": accuracy,
+        "orthography": orthography,
+        "meaning": meaning,
+        "errors": errors,
+        "performance": performance,
+        "improvement": improvement,
+        "usability": usability,
+        "technical_issues": technical_issues,
+        "final_comments": final_comments,
+        "email": email
+    }
+
+    try:
+        response = requests.post(BACKEND_URL, json=data, timeout=10)
+        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", "Other"], label="Gender")
+    evaluated_language = gr.Dropdown(list(model_map.keys()), label="Which language did you evaluate for?")
+    speak_level = gr.Slider(1, 10, label="How well do you speak this language?")
+    write_level = gr.Slider(1, 10, 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 not, what is your native language?")
+    env = gr.Dropdown(["Studio/Professional Recording", "Quiet Room", "Noisy Background", "Multiple Environments", "Unsure", "Other"], label="Recording environment")
+    device = gr.Dropdown(["Mobile Phone/Tablet", "Tablet", "Laptop/Computer Microphone", "Dedicated Microphone", "Unsure", "Other"], label="Recording device")
+    domain = gr.Textbox(label="Was the speech related to a specific domain or topic? (Optional)")
+    accuracy = gr.Slider(1, 10, label="How accurate was the model’s transcription?")
+    orthography = gr.Dropdown(["Yes, mostly correct", "No, major issues", "Partially", "Not Applicable"], label="Did the transcription use standard orthography?")
+    meaning = gr.Slider(1, 10, label="Did the transcription preserve the original meaning?")
+    errors = gr.CheckboxGroup([
+        "Substitutions", "Omissions", "Insertions", "Pronunciation-related", "Diacritic Errors",
+        "Code-switching Errors", "Named Entity Errors", "Punctuation Errors", "No significant errors"
+    ], label="Which errors were prominent?")
+    performance = gr.Textbox(label="What did the model do well? What did it struggle with?")
+    improvement = gr.Textbox(label="How could this ASR model be improved?")
+    usability = gr.Slider(1, 5, label="How easy was it to use the tool?")
+    technical_issues = gr.Textbox(label="Did you encounter any technical issues?")
+    final_comments = gr.Textbox(label="Any other comments or suggestions?")
+    email = gr.Textbox(label="Email (optional)")
+
+    save_btn = gr.Button("Submit Feedback")
+    output_msg = gr.Textbox(interactive=False)
+    save_btn.click(fn=save_feedback, 
+                   inputs=[audio_input, transcribed_text, age_group, gender, evaluated_language, speak_level, write_level,
+                           native, native_language, env, device, domain, accuracy, orthography, meaning, errors,
+                           performance, improvement, usability, technical_issues, final_comments, email],
+                   outputs=[output_msg])
+
+# Launch the interface
+demo.launch()