Update app.py

app.py

@@ -2,6 +2,9 @@ import os
 import streamlit as st
 import pandas as pd
 from groq import Groq
+import sounddevice as sd
+import numpy as np
+import wavio
 
 # Set your API key (replace with secure method for production)
 API_KEY = "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"
@@ -24,7 +27,7 @@ if st.sidebar.button("Register"):
 
 # Diagnostic Test
 st.header("Diagnostic Test")
-assessment_text = st.text_area("Read the following text aloud:", "Sample text for assessment...")
+assessment_text = st.text_area("Read the following text aloud:", "Sample text for assessment: The quick brown fox jumps over the lazy dog.")
 if st.button("Submit Assessment"):
     if assessment_text:
         # Call the LLAMA model for feedback based on assessment
@@ -45,32 +48,41 @@ if st.button("Submit Assessment"):
 # Personalized Reading Material
 st.header("Generate Personalized Reading Material")
 if st.button("Generate Reading Material"):
-    generated_text = "Generated personalized text focusing on phonetic patterns."  # Placeholder for personalized content
+    generated_text = "Generated personalized text focusing on phonetic patterns: 'sh', 'ch', and 'th'."  # Placeholder for personalized content
     st.text_area("Your Personalized Reading Material:", generated_text, height=200)
 
-# User Feedback
-st.header("Feedback Collection")
-feedback = st.text_area("Your Feedback:")
-if st.button("Submit Feedback"):
-    if feedback:
-        # Placeholder for storing feedback (e.g., to Google Sheets)
-        st.success("Feedback submitted successfully!")
-    else:
-        st.error("Please provide your feedback.")
+# Audio Recording Feature
+st.header("Record Your Voice")
+duration = st.number_input("Duration of recording (seconds)", min_value=1, max_value=60, value=5)
+if st.button("Record Audio"):
+    st.write("Recording...")
+    recording = sd.rec(int(duration * 44100), samplerate=44100, channels=2)
+    sd.wait()  # Wait until the recording is finished
+    wavio.write("recording.wav", recording, 44100, sampwidth=2)  # Save as WAV file
+    st.success("Recording completed!")
 
-# Audio Transcription
-st.header("Audio Transcription")
-filename = st.file_uploader("Upload your audio file (m4a):", type=["m4a"])
-if filename is not None:
+# Upload Recorded Audio for Transcription
+if st.file_uploader("Upload your audio file (m4a):", type=["m4a"]) is not None:
     if st.button("Transcribe Audio"):
+        filename = "recording.wav"  # Use the recorded file for transcription
         with open(filename, "rb") as file:
             transcription = client.audio.transcriptions.create(
-                file=(filename.name, file.read()),
+                file=(filename, file.read()),
                 model="whisper-large-v3",
                 response_format="verbose_json",
             )
             st.write("Transcription:", transcription.text)
 
+# Feedback Collection
+st.header("Feedback Collection")
+feedback = st.text_area("Your Feedback:")
+if st.button("Submit Feedback"):
+    if feedback:
+        # Placeholder for storing feedback (e.g., to Google Sheets)
+        st.success("Feedback submitted successfully!")
+    else:
+        st.error("Please provide your feedback.")
+
 # Visualization (User Progress)
 st.header("User Progress Visualization")
 # Placeholder for progress data visualization