Update app.py

app.py

@@ -1,165 +1,165 @@
-import streamlit as st
-from google.oauth2 import service_account
-from google.cloud import speech
-import io
-import torch
-import numpy as np
-from transformers import Wav2Vec2Processor
-from transformers.models.wav2vec2 import Wav2Vec2Model
-import librosa
-from groq import Groq
-
-# Initialize Google Speech-to-Text, Hugging Face model, and Groq LLM
-client_file = "gcp_api.json"  # Replace with your actual service account file path
-credentials = service_account.Credentials.from_service_account_file(client_file)
-speech_client = speech.SpeechClient(credentials=credentials)
-
-processor = Wav2Vec2Processor.from_pretrained("models/wav2vec2-base")
-model = Wav2Vec2Model.from_pretrained("models/wav2vec2-base")
-
-ideal_embedding = torch.tensor(np.load("ideal_azan_embedding.npy"))
-
-groq_client = Groq(api_key="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")
-
-# Define the ideal Azan text (first part only) and its English meaning
-ideal_text = "اللّٰهُ أَكْبَرُ، اللّٰهُ أَكْبَرُ"
-ideal_text_meaning = "Allah is the Greatest, Allah is the Greatest"
-
-# Function to extract embedding of the uploaded audio
-def get_audio_embedding(audio_file_path):
-    audio_input, _ = librosa.load(audio_file_path, sr=16000)
-    inputs = processor(audio_input, sampling_rate=16000, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        embedding = model(inputs.input_values).last_hidden_state.mean(dim=1).squeeze()
-    return embedding
-
-# Function to calculate similarity score between user and ideal embeddings
-def calculate_similarity(embedding1, embedding2):
-    similarity = torch.nn.functional.cosine_similarity(embedding1, embedding2, dim=0)
-    return similarity.item() * 100
-
-# Function to generate feedback with Groq LLM
-def generate_feedback_with_llm(user_transcription, ideal_text, similarity_score): 
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are an expert muezzin trainer providing detailed, supportive feedback on a student's Azan transcription accuracy. "
-                "The student has attempted to recite the phrase, and their transcription is compared to the ideal Azan phrase in Arabic. "
-                "Evaluate how closely their recitation matches the ideal Azan text based on articulation, tone, rhythm, and accuracy. "
-                "Emphasize strengths, point out specific areas where improvements can be made, and give clear, practical tips to improve pronunciation. "
-                "Encourage the student with positivity, helping them refine their pronunciation and accuracy until it aligns closely with the ideal."
-            )
-        },
-        {
-            "role": "user",
-            "content": (
-                f"The student's transcription of their recitation is: '{user_transcription}'. "
-                f"The ideal phrase for comparison is: '{ideal_text}'. Their similarity score is {similarity_score:.2f}%. "
-                "Please provide feedback highlighting strengths, improvement areas, and actionable tips for better alignment with the ideal."
-            )
-        }
-    ]
-    completion = groq_client.chat.completions.create(
-        model="llama3-70b-8192",
-        messages=messages,
-        temperature=0.7,
-        max_tokens=1000
-    )
-    return completion.choices[0].message.content
-
-# Function to transcribe audio, validate with the ideal text, and provide feedback
-def transcribe_and_validate(audio_file_path, ideal_text):
-    with io.open(audio_file_path, 'rb') as f:
-        audio_content = f.read()
-    audio = speech.RecognitionAudio(content=audio_content)
-    config = speech.RecognitionConfig(
-        encoding=speech.RecognitionConfig.AudioEncoding.MP3,
-        sample_rate_hertz=48000,
-        language_code="ar"
-    )
-    response = speech_client.recognize(config=config, audio=audio)
-    transcription = " ".join(result.alternatives[0].transcript for result in response.results)
-
-    # Refined prompt for validation with LLM
-    content = f"""
-        You are an expert in validating the Azaan (the call to prayer). Below is the correct structure of the Azaan. 
-        Compare the transcription provided with this structure to determine if it contains all essential phrases in the correct order.
-
-        Validation Guidelines:
-        - Validate the Azaan as "VALIDATED" if it contains all essential phrases in the correct sequence, even if there are minor spelling, diacritic, or punctuation differences.
-        - Specifically, ignore small differences such as:
-            - Missing or extra diacritics (e.g., "ا" vs. "أ" or "حي على الصلاه" vs. "حي على الصلاة").
-            - Minor spelling variations, such as:
-                - "لا اله الا الله" vs. "لا إله إلا الله".
-                - "حي على الصلاه" vs. "حي على الصلاة".
-                - "حي على الفلا��" vs. "حي على الفلاح".
-                - "أشهد" vs "شهاده"
-            - Punctuation or slight variations in commonly understood words and phrases.
-        - Invalidate the Azaan as "INVALIDATED" only if:
-            - Essential phrases are missing.
-            - Extra, unrelated phrases that are not part of the Azaan are added.
-            - Major incorrect words or substitutions that change the meaning of an essential phrase are present.
-
-        Correct Azaan Structure:
-        "{ideal_text}"
-
-        Transcribed Azaan:
-        "{transcription}"
-
-        Conclude with "Validation Status: VALIDATED" if the Azaan matches the correct structure, or "Validation Status: INVALIDATED" if it does not, and list any specific issues if found. Only list issues if they involve missing phrases, extra phrases, or significant meaning changes.
-    """
-
-    # Send request to Groq LLM for validation feedback
-    completion = groq_client.chat.completions.create(
-        model="llama3-70b-8192",
-        messages=[{"role": "user", "content": content}],
-        temperature=0,
-        max_tokens=512,
-    )
-    feedback = completion.choices[0].message.content
-
-    return transcription, feedback
-
-# Streamlit layout
-st.set_page_config(page_title="Azan Pronunciation Trainer", layout="centered", initial_sidebar_state="expanded")
-
-# Display ideal text and its meaning for reference
-st.markdown("<div style='font-size: 30px; color: #4CAF50; text-align: center;'>Azan Pronunciation Trainer</div>", unsafe_allow_html=True)
-st.markdown(f"<div style='font-size: 20px; text-align: center;'>Phrase to Practice: {ideal_text}</div>", unsafe_allow_html=True)
-st.markdown(f"<div style='font-size: 18px; text-align: center; color: #555;'>Meaning: {ideal_text_meaning}</div>", unsafe_allow_html=True)
-
-# Placeholder for expert audio playback
-st.audio(r"C:\Users\USER\Downloads\wav2vec\expert_azan_audio.mp3", format="audio/mp3")  # Replace with actual path
-
-# Upload audio file for pronunciation assessment
-st.markdown("<div style='font-size: 18px; text-align: center;'>Upload your Azan recitation audio (MP3 format):</div>", unsafe_allow_html=True)
-audio_file = st.file_uploader("Choose an audio file", type=["mp3"])
-
-if audio_file is not None:
-    with st.spinner("Analyzing your pronunciation..."):
-        audio_path = "uploaded_audio.mp3"
-        with open(audio_path, "wb") as f:
-            f.write(audio_file.read())
-
-        # Transcribe and validate transcription with the ideal text
-        transcription, validation_feedback = transcribe_and_validate(audio_path, ideal_text)
-
-        # Check if validation is successful
-        if "Validation Status: VALIDATED" in validation_feedback:
-            # Perform similarity check if validated
-            user_embedding = get_audio_embedding(audio_path)
-            similarity_score = calculate_similarity(user_embedding, ideal_embedding)
-            
-            st.markdown(f"<div style='font-size: 18px; color: #333; padding: 10px; border: 2px solid #4CAF50; border-radius: 10px; background-color: #f9f9f9;'><b>Similarity Score:</b> {similarity_score:.2f}%</div>", unsafe_allow_html=True)
-
-            # Provide feedback based on similarity score
-            if similarity_score >= 90:
-                st.markdown("<div style='color: green; font-weight: bold;'>Excellent work! Your pronunciation is reverent and accurate. You may proceed to the next phrase.</div>", unsafe_allow_html=True)
-            else:
-                llm_feedback = generate_feedback_with_llm(transcription, ideal_text, similarity_score)
-                st.markdown(f"<div style='font-size: 18px; color: #333; padding: 10px; border: 2px solid #4CAF50; border-radius: 10px; background-color: #f9f9f9;'><b>Feedback:</b><br>{llm_feedback}</div>", unsafe_allow_html=True)
-        else:
-            # Inform user to re-record if validation failed
-            st.markdown("<div style='color: red; font-weight: bold;'>The transcription does not match the ideal Azan phrase. Please record your recitation again.</div>", unsafe_allow_html=True)
-            st.markdown(f"<div style='font-size: 18px; color: #333; padding: 10px; border: 2px solid #4CAF50; border-radius: 10px; background-color: #f9f9f9;'><b>Feedback:</b><br>{validation_feedback}</div>", unsafe_allow_html=True)
+import streamlit as st
+from google.oauth2 import service_account
+from google.cloud import speech
+import io
+import torch
+import numpy as np
+from transformers import Wav2Vec2Processor
+from transformers.models.wav2vec2 import Wav2Vec2Model
+import librosa
+from groq import Groq
+
+# Initialize Google Speech-to-Text, Hugging Face model, and Groq LLM
+client_file = "gcp_api.json"  # Replace with your actual service account file path
+credentials = service_account.Credentials.from_service_account_file(client_file)
+speech_client = speech.SpeechClient(credentials=credentials)
+
+processor = Wav2Vec2Processor.from_pretrained("models/wav2vec2-base")
+model = Wav2Vec2Model.from_pretrained("models/wav2vec2-base")
+
+ideal_embedding = torch.tensor(np.load("ideal_azan_embedding.npy"))
+
+groq_client = Groq(api_key="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")
+
+# Define the ideal Azan text (first part only) and its English meaning
+ideal_text = "اللّٰهُ أَكْبَرُ، اللّٰهُ أَكْبَرُ"
+ideal_text_meaning = "Allah is the Greatest, Allah is the Greatest"
+
+# Function to extract embedding of the uploaded audio
+def get_audio_embedding(audio_file_path):
+    audio_input, _ = librosa.load(audio_file_path, sr=16000)
+    inputs = processor(audio_input, sampling_rate=16000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        embedding = model(inputs.input_values).last_hidden_state.mean(dim=1).squeeze()
+    return embedding
+
+# Function to calculate similarity score between user and ideal embeddings
+def calculate_similarity(embedding1, embedding2):
+    similarity = torch.nn.functional.cosine_similarity(embedding1, embedding2, dim=0)
+    return similarity.item() * 100
+
+# Function to generate feedback with Groq LLM
+def generate_feedback_with_llm(user_transcription, ideal_text, similarity_score): 
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are an expert muezzin trainer providing detailed, supportive feedback on a student's Azan transcription accuracy. "
+                "The student has attempted to recite the phrase, and their transcription is compared to the ideal Azan phrase in Arabic. "
+                "Evaluate how closely their recitation matches the ideal Azan text based on articulation, tone, rhythm, and accuracy. "
+                "Emphasize strengths, point out specific areas where improvements can be made, and give clear, practical tips to improve pronunciation. "
+                "Encourage the student with positivity, helping them refine their pronunciation and accuracy until it aligns closely with the ideal."
+            )
+        },
+        {
+            "role": "user",
+            "content": (
+                f"The student's transcription of their recitation is: '{user_transcription}'. "
+                f"The ideal phrase for comparison is: '{ideal_text}'. Their similarity score is {similarity_score:.2f}%. "
+                "Please provide feedback highlighting strengths, improvement areas, and actionable tips for better alignment with the ideal."
+            )
+        }
+    ]
+    completion = groq_client.chat.completions.create(
+        model="llama3-70b-8192",
+        messages=messages,
+        temperature=0.7,
+        max_tokens=1000
+    )
+    return completion.choices[0].message.content
+
+# Function to transcribe audio, validate with the ideal text, and provide feedback
+def transcribe_and_validate(audio_file_path, ideal_text):
+    with io.open(audio_file_path, 'rb') as f:
+        audio_content = f.read()
+    audio = speech.RecognitionAudio(content=audio_content)
+    config = speech.RecognitionConfig(
+        encoding=speech.RecognitionConfig.AudioEncoding.MP3,
+        sample_rate_hertz=48000,
+        language_code="ar"
+    )
+    response = speech_client.recognize(config=config, audio=audio)
+    transcription = " ".join(result.alternatives[0].transcript for result in response.results)
+
+    # Refined prompt for validation with LLM
+    content = f"""
+        You are an expert in validating the Azaan (the call to prayer). Below is the correct structure of the Azaan. 
+        Compare the transcription provided with this structure to determine if it contains all essential phrases in the correct order.
+
+        Validation Guidelines:
+        - Validate the Azaan as "VALIDATED" if it contains all essential phrases in the correct sequence, even if there are minor spelling, diacritic, or punctuation differences.
+        - Specifically, ignore small differences such as:
+            - Missing or extra diacritics (e.g., "ا" vs. "أ" or "حي على الصلاه" vs. "حي على الصلاة").
+            - Minor spelling variations, such as:
+                - "لا اله الا الله" vs. "لا إله إلا الله".
+                - "حي على الصلاه" vs. "حي على الصلاة".
+                - "حي على الفلاح" vs. "حي على الفلاح".
+                - "أشهد" vs "شهاده"
+            - Punctuation or slight variations in commonly understood words and phrases.
+        - Invalidate the Azaan as "INVALIDATED" only if:
+            - Essential phrases are missing.
+            - Extra, unrelated phrases that are not part of the Azaan are added.
+            - Major incorrect words or substitutions that change the meaning of an essential phrase are present.
+
+        Correct Azaan Structure:
+        "{ideal_text}"
+
+        Transcribed Azaan:
+        "{transcription}"
+
+        Conclude with "Validation Status: VALIDATED" if the Azaan matches the correct structure, or "Validation Status: INVALIDATED" if it does not, and list any specific issues if found. Only list issues if they involve missing phrases, extra phrases, or significant meaning changes.
+    """
+
+    # Send request to Groq LLM for validation feedback
+    completion = groq_client.chat.completions.create(
+        model="llama3-70b-8192",
+        messages=[{"role": "user", "content": content}],
+        temperature=0,
+        max_tokens=512,
+    )
+    feedback = completion.choices[0].message.content
+
+    return transcription, feedback
+
+# Streamlit layout
+st.set_page_config(page_title="Azan Pronunciation Trainer", layout="centered", initial_sidebar_state="expanded")
+
+# Display ideal text and its meaning for reference
+st.markdown("<div style='font-size: 30px; color: #4CAF50; text-align: center;'>Azan Pronunciation Trainer</div>", unsafe_allow_html=True)
+st.markdown(f"<div style='font-size: 20px; text-align: center;'>Phrase to Practice: {ideal_text}</div>", unsafe_allow_html=True)
+st.markdown(f"<div style='font-size: 18px; text-align: center; color: #555;'>Meaning: {ideal_text_meaning}</div>", unsafe_allow_html=True)
+
+# Placeholder for expert audio playback
+st.audio("expert_azan_audio.mp3", format="audio/mp3")  # Replace with actual path
+
+# Upload audio file for pronunciation assessment
+st.markdown("<div style='font-size: 18px; text-align: center;'>Upload your Azan recitation audio (MP3 format):</div>", unsafe_allow_html=True)
+audio_file = st.file_uploader("Choose an audio file", type=["mp3"])
+
+if audio_file is not None:
+    with st.spinner("Analyzing your pronunciation..."):
+        audio_path = "uploaded_audio.mp3"
+        with open(audio_path, "wb") as f:
+            f.write(audio_file.read())
+
+        # Transcribe and validate transcription with the ideal text
+        transcription, validation_feedback = transcribe_and_validate(audio_path, ideal_text)
+
+        # Check if validation is successful
+        if "Validation Status: VALIDATED" in validation_feedback:
+            # Perform similarity check if validated
+            user_embedding = get_audio_embedding(audio_path)
+            similarity_score = calculate_similarity(user_embedding, ideal_embedding)
+            
+            st.markdown(f"<div style='font-size: 18px; color: #333; padding: 10px; border: 2px solid #4CAF50; border-radius: 10px; background-color: #f9f9f9;'><b>Similarity Score:</b> {similarity_score:.2f}%</div>", unsafe_allow_html=True)
+
+            # Provide feedback based on similarity score
+            if similarity_score >= 90:
+                st.markdown("<div style='color: green; font-weight: bold;'>Excellent work! Your pronunciation is reverent and accurate. You may proceed to the next phrase.</div>", unsafe_allow_html=True)
+            else:
+                llm_feedback = generate_feedback_with_llm(transcription, ideal_text, similarity_score)
+                st.markdown(f"<div style='font-size: 18px; color: #333; padding: 10px; border: 2px solid #4CAF50; border-radius: 10px; background-color: #f9f9f9;'><b>Feedback:</b><br>{llm_feedback}</div>", unsafe_allow_html=True)
+        else:
+            # Inform user to re-record if validation failed
+            st.markdown("<div style='color: red; font-weight: bold;'>The transcription does not match the ideal Azan phrase. Please record your recitation again.</div>", unsafe_allow_html=True)
+            st.markdown(f"<div style='font-size: 18px; color: #333; padding: 10px; border: 2px solid #4CAF50; border-radius: 10px; background-color: #f9f9f9;'><b>Feedback:</b><br>{validation_feedback}</div>", unsafe_allow_html=True)