Update app.py

app.py

@@ -9,25 +9,24 @@ import pandas as pd
 # Sample dataframe with sentences
 data = {
     "Sentences": [
-        "A stitch in time saves nine.",
+        "The quick brown fox jumps over the lazy dog.",
+        "An apple a day keeps the doctor away.",
         "To be or not to be, that is the question.",
-        "Five cats were living in safe caves.",
-        "Hives give shelter to bees in large caves.",
-        "His decision to plant a rose was amazing.",
+        "All human beings are born free and equal in dignity and rights.",
         "She sells sea shells by the sea shore.",
-        "The colorful parrot likes rolling berries.",
-        "Time flies like an arrow; fruit flies like a banana.",
+        "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
+        "A stitch in time saves nine.",
         "Good things come to those who wait.",
-        "All human beings are born free and equal in dignity and rights."
+        "Time flies like an arrow; fruit flies like a banana.",
+        "You can't judge a book by its cover."
     ]
 }
 df = pd.DataFrame(data)
-user_scores = {}
 
 def transcribe_audio(file_info):
     r = sr.Recognizer()
     with tempfile.NamedTemporaryFile(delete=True, suffix=".wav") as tmpfile:
-        sf.write(tmpfile.name, data=file_info[1], samplerate=44100, format='WAV')
+        sf.write(file=tmpfile.name, data=file_info[1], samplerate=44100, format='WAV')
         tmpfile.seek(0)
         with sr.AudioFile(tmpfile.name) as source:
             audio_data = r.record(source)
@@ -39,12 +38,36 @@ def transcribe_audio(file_info):
     except sr.RequestError as e:
         return f"Could not request results; {e}"
 
-def pronunciation_correction(name, expected_text, file_info):
+def pronunciation_correction(selected_sentence, file_info):
+    expected_text = selected_sentence
     user_spoken_text = transcribe_audio(file_info)
     similarity = ratio(expected_text.lower(), user_spoken_text.lower())
-    score = float(f"{similarity:.2f}")
-    if name in user_scores:
-        user_scores[name].append(score)  # Track scores for each user
+    description = f"{similarity:.2f}"  # Formats the float to 2 decimal places
+
+    if similarity >= 0.9:
+        feedback = "Excellent pronunciation!"
+    elif similarity >= 0.7:
+        feedback = "Good pronunciation!"
+    elif similarity >= 0.5:
+        feedback = "Needs improvement."
     else:
-        user_scores[name] = [score]
-    feedback = "Excellent pr
+        feedback = "Poor pronunciation, try to focus more on clarity."
+
+    return feedback, description
+
+
+iface = gr.Interface(
+    fn=pronunciation_correction,
+    inputs=[
+        gr.Dropdown(choices=df['Sentences'].tolist(), label="Select a Sentence"),
+        gr.Audio(label="Upload Audio File", type="numpy")
+    ],
+    outputs=[
+        gr.Textbox(label="Pronunciation Feedback"),  # Custom label for the text output
+        gr.Number(label="Pronunciation Accuracy Score: 0 (No Match) ~ 1 (Perfect)")  # Custom label for the numerical output
+    ],
+    title="🌀 Pronunciation Feedback Tool"
+)
+
+
+iface.launch(debug=True)