Update app.py

app.py

@@ -7,7 +7,6 @@ from gtts import gTTS
 import io
 from pydub import AudioSegment
 import time
-import pronouncing
 import epitran
 
 # Create audio directory if it doesn't exist
@@ -20,6 +19,27 @@ try:
 except Exception as e:
     print(f"Error initializing Epitran: {e}")
 
+# Step 2: Create pronunciation audio for incorrect words
+def upfilepath(local_filename):
+    ts = time.time()
+    upload_url = f"https://mr2along-speech-recognize.hf.space/gradio_api/upload?upload_id={ts}"
+    files = {'files': open(local_filename, 'rb')}
+
+    try:
+        response = requests.post(upload_url, files=files, timeout=30)  # Set timeout (e.g., 30 seconds)
+        
+        if response.status_code == 200:
+            result = response.json()
+            extracted_path = result[0]
+            return extracted_path
+        else:
+            return None
+
+    except requests.exceptions.Timeout:
+        return "Request timed out. Please try again."
+    except Exception as e:
+        return f"An error occurred: {e}"
+
 # Step 1: Transcribe the audio file
 def transcribe_audio(audio):
     if audio is None:
@@ -54,6 +74,13 @@ def transcribe_audio(audio):
     except sr.RequestError as e:
         return f"Error with Google Speech Recognition service: {e}"
 
+# Function to get IPA transcription
+def ipa_transcription(sentence):
+    try:
+        return epi.transliterate(sentence)
+    except Exception as e:
+        return f"Error during IPA transcription: {e}"
+
 # Step 2: Create pronunciation audio for incorrect words (locally)
 def create_pronunciation_audio(word):
     try:
@@ -64,38 +91,6 @@ def create_pronunciation_audio(word):
     except Exception as e:
         return f"Failed to create pronunciation audio: {e}"
 
-# Function for phonetic respelling
-def phonetic_respelling(sentence):
-    words = sentence.split()
-    respelled = []
-    
-    for word in words:
-        # Find close matches for each word
-        close_matches = pronouncing.search(word)
-        if close_matches:
-            # Get the first close match
-            closest_word = close_matches[0]
-            respelled.append(pronouncing.phones_for_word(closest_word)[0])  # Use phonemes for the closest match
-        else:
-            respelled.append(word)
-
-    # Convert phonemes to respelling
-    respelling = ' '.join(respelled)
-    
-    # Replace phonemes with common respellings
-    respelling = respelling.replace('ˈ', '').replace('ˌ', '').replace('ː', '')  # Clean up phoneme symbols
-    respelling = respelling.replace('ɑ', 'a').replace('ə', 'uh').replace('ɪ', 'i').replace('ʊ', 'u')  # Sample conversions
-    
-    return respelling
-
-# Function for IPA transcription
-def ipa_transcription(sentence):
-    try:
-        return epi.transliterate(sentence)
-    except Exception as e:
-        print(f"Error during IPA transcription: {e}")
-        return "IPA transcription failed."
-
 # Step 3: Compare the transcribed text with the input paragraph
 def compare_texts(reference_text, transcribed_text):
     reference_words = reference_text.split()
@@ -118,9 +113,7 @@ def compare_texts(reference_text, transcribed_text):
 
     html_output += f"<strong>Quality Score:</strong> {similarity_score}%<br>"
     html_output += f"<strong>Transcribed Text:</strong> {transcribed_text}<br>"
-    html_output += f"<strong>Input Sentence:</strong> {reference_text}<br>"
-    html_output += f"<strong>Phonetic Respelling:</strong> {phonetic_respelling(reference_text)}<br>"
-    html_output += f"<strong>IPA Transcription:</strong> {ipa_transcription(reference_text)}<br>"
+    html_output += f"<strong>IPA Transcription:</strong> {ipa_transcription(reference_text)}<br>"  # Display IPA transcription
     html_output += "<strong>Word Score List:</strong><br>"
 
     # Generate colored word score list