Update app.py

app.py

@@ -1,3 +1,4 @@
+#@markdown Accuracy Score, Average, user name
 import gradio as gr
 import speech_recognition as sr
 from Levenshtein import ratio
@@ -9,24 +10,25 @@ import pandas as pd
 # Sample dataframe with sentences
 data = {
     "Sentences": [
-        "The quick brown fox jumps over the lazy dog.",
-        "An apple a day keeps the doctor away.",
+        "A stitch in time saves nine.",
         "To be or not to be, that is the question.",
-        "All human beings are born free and equal in dignity and rights.",
+        "Five cats were living in safe caves.",
+        "Hives give shelter to bees in large caves.",
+        "His decision to plant a rose was amazing.",
         "She sells sea shells by the sea shore.",
-        "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.",
+        "The colorful parrot likes rolling berries.",
         "Time flies like an arrow; fruit flies like a banana.",
-        "You can't judge a book by its cover."
+        "Good things come to those who wait.",
+        "All human beings are born free and equal in dignity and rights."
     ]
 }
 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(file=tmpfile.name, data=file_info[1], samplerate=44100, format='WAV')
+        sf.write(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)
@@ -38,36 +40,50 @@ def transcribe_audio(file_info):
     except sr.RequestError as e:
         return f"Could not request results; {e}"
 
-def pronunciation_correction(selected_sentence, file_info):
-    expected_text = selected_sentence
+def pronunciation_correction(name, expected_text, file_info):
     user_spoken_text = transcribe_audio(file_info)
     similarity = ratio(expected_text.lower(), user_spoken_text.lower())
-    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."
+    score = float(f"{similarity:.2f}")
+    if name in user_scores:
+        user_scores[name].append(score)  # Track scores for each user
     else:
-        feedback = "Poor pronunciation, try to focus more on clarity."
-
-    return feedback, description
+        user_scores[name] = [score]
+    feedback = "Excellent pronunciation!" if score >= 0.9 else \
+               "Good pronunciation!" if score >= 0.7 else \
+               "Needs improvement." if score >= 0.5 else \
+               "Poor pronunciation, try to focus more on clarity."
+    return feedback, score
 
+def calculate_average(name):
+    if name in user_scores and user_scores[name]:
+        filtered_scores = [score for score in user_scores[name] if score > 0]  # Ignore zeros
+        average_score = sum(filtered_scores) / len(filtered_scores)
+    else:
+        average_score = 0
+    return f"😍 Great job, {name}! \n\nYour average score (excluding zeros) is: {average_score:.2f}. \nRemember, this score only focuses on the accuracy of individual sounds. \nKeep up the fun and enjoyment as you continue learning English!"
 
-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"
-)
+with gr.Blocks() as app:
+    name_input = gr.Textbox(label="Enter your name", placeholder="Type your name here...", value="")
+    with gr.Row():
+        sentence_dropdown = gr.Dropdown(choices=df['Sentences'].tolist(), label="Select a Sentence")
+        selected_sentence_output = gr.Textbox(label="Selected Text", interactive=False)
+    audio_input = gr.Audio(label="Upload Audio File", type="numpy")
+    check_pronunciation_button = gr.Button("Check Pronunciation")
+    pronunciation_feedback = gr.Textbox(label="Pronunciation Feedback")
+    pronunciation_score = gr.Number(label="Pronunciation Accuracy Score: 0 (No Match) ~ 1 (Perfect)")
+    complete_button = gr.Button("Complete")
+    average_output = gr.Textbox(label="Average Score Output", visible=True)
 
+    sentence_dropdown.change(lambda x: x, inputs=sentence_dropdown, outputs=selected_sentence_output)
+    check_pronunciation_button.click(
+        pronunciation_correction,
+        inputs=[name_input, sentence_dropdown, audio_input],
+        outputs=[pronunciation_feedback, pronunciation_score]
+    )
+    complete_button.click(
+        calculate_average,
+        inputs=[name_input],
+        outputs=average_output
+    )
 
-iface.launch(share=True, debug=True)
+app.launch(debug=True)