Create app.py

app.py

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+import os
+import streamlit as st
+import pandas as pd
+from groq import Groq
+
+# Set your API key directly (for demonstration purposes only)
+API_KEY = "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"
+
+# Initialize Groq client with the API key
+client = Groq(api_key=API_KEY)
+
+# Streamlit app layout
+st.set_page_config(page_title="PhoneX: Personalized Reading App", layout="wide")
+st.title("Welcome to PhoneX: Your Personalized Reading App")
+st.sidebar.title("Navigation")
+
+# User Registration
+user_id = st.sidebar.text_input("Enter your User ID:")
+if st.sidebar.button("Register"):
+    if user_id:
+        st.sidebar.success(f"User {user_id} registered successfully!")
+    else:
+        st.sidebar.error("Please enter a valid User ID.")
+
+# Initial Reading Assessment
+st.header("Initial Reading Assessment")
+assessment_text = st.text_area("Read the following text aloud:", "Sample text for assessment...")
+if st.button("Submit Assessment"):
+    if assessment_text:
+        # Call the LLAMA model for chat completion based on the assessment
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Evaluate the following assessment: {assessment_text}",
+                }
+            ],
+            model="llama3-8b-8192",
+        )
+        st.success("Assessment submitted successfully!")
+        st.write("LLAMA Model Output:", chat_completion.choices[0].message.content)
+    else:
+        st.error("Please enter the text to assess.")
+
+# Generate Reading Material
+st.header("Generate Personalized Reading Material")
+if st.button("Generate Reading Material"):
+    generated_text = "Generated personalized text based on assessment."  # Placeholder
+    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 in Google Sheets
+        st.success("Feedback submitted successfully!")
+    else:
+        st.error("Please provide your feedback.")
+
+# Audio Transcription
+st.header("Audio Transcription")
+filename = st.file_uploader("Upload your audio file (m4a):", type=["m4a"])
+if filename is not None:
+    if st.button("Transcribe Audio"):
+        with open(filename, "rb") as file:
+            transcription = client.audio.transcriptions.create(
+                file=(filename.name, file.read()),
+                model="whisper-large-v3",
+                response_format="verbose_json",
+            )
+            st.write("Transcription:", transcription.text)
+
+# Visualization (Using Plotly or Streamlit's built-in charting capabilities)
+st.header("User Progress Visualization")
+# Placeholder for progress data visualization
+progress_data = pd.DataFrame({'Task': ['Reading', 'Feedback'], 'Scores': [85, 90]})
+st.bar_chart(progress_data.set_index('Task'))
+
+# Error Handling Example
+try:
+    # Your main logic here
+    pass
+except Exception as e:
+    st.error(f"An error occurred: {e}")
+
+# Footer
+st.sidebar.markdown("---")
+st.sidebar.markdown("Created by Your Name")