Update app.py

app.py

@@ -2,7 +2,7 @@
 import gradio as gr
 import openai
 import os
-import RadinMas  # Importing the RadinMas module
+import AlpsData  # Importing the AlpsData module
 import base64
 
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
@@ -12,7 +12,7 @@ def image_to_base64(img_path):
     with open(img_path, "rb") as img_file:
         return base64.b64encode(img_file.read()).decode('utf-8')
 
-img_base64 = image_to_base64("RadinMasSBC.JPG")
+img_base64 = image_to_base64("AlpsSBC.JPG")
 img_html = f'<img src="data:image/jpg;base64,{img_base64}" alt="SBC6" width="300" style="display: block; margin: auto;"/>'
 
 def predict(question_choice, feedback_level, audio):
@@ -22,23 +22,23 @@ def predict(question_choice, feedback_level, audio):
     message = transcript["text"]  # This is the transcribed message from the audio input
 
     # Generate the prompt based on the feedback level
-    feedback_prompt = RadinMas.generate_prompt(feedback_level) 
+    feedback_prompt = AlpsData.generate_prompt(feedback_level) 
     
     # Determine question number based on question_choice
-    question_number = RadinMas.questions.index(question_choice) + 1  # New line
+    question_number = AlpsData.questions.index(question_choice) + 1  # New line
 
     # Generate the system message based on the question number
-    system_message = RadinMas.generate_system_message(question_number, feedback_level)  # Updated line to include feedback_level
+    system_message = AlpsData.generate_system_message(question_number, feedback_level)  # Updated line to include feedback_level
 
     
-    # Reference to the picture description from RadinMas.py
-    picture_description = RadinMas.description
+    # Reference to the picture description from AlpsData.py
+    picture_description = AlpsData.description
 
     # Determine whether to include the picture description based on the question choice
     picture_description_inclusion = f"""
     For the first question, ensure your feedback refers to the picture description provided:
     {picture_description}
-    """ if question_choice == RadinMas.questions[0] else ""
+    """ if question_choice == AlpsData.questions[0] else ""
 
     # Construct the conversation with the system and user's message
     conversation = [
@@ -68,24 +68,22 @@ def predict(question_choice, feedback_level, audio):
             partial_message = partial_message + chunk['choices'][0]['delta']['content']
             yield partial_message
 
-# Gradio Interface
 iface = gr.Interface(
     fn=predict,
     inputs=[
-        gr.Radio(RadinMas.questions, label="Choose a question", default=RadinMas.questions[0]),  # Dropdown for question choice
-        gr.Radio(["Brief Feedback", "Moderate Feedback", "Comprehensive Feedback"], label="Choose a feedback level", default="Brief Feedback"),  # Ensure this line is present
-        gr.inputs.Audio(source="microphone", type="filepath")  # Audio input
+        gr.Radio(AlpsData.questions, label="Choose a question", default=AlpsData.questions[0]),  # Updated reference
+        gr.Radio(["Brief Feedback", "Moderate Feedback", "Comprehensive Feedback"], label="Choose a feedback level", default="Brief Feedback"),
+        gr.inputs.Audio(source="microphone", type="filepath")
     ],
-    outputs=gr.inputs.Textbox(),  # Using inputs.Textbox as an output to make it editable
+    outputs=gr.inputs.Textbox(),
     description=img_html + '''
         <div style="text-align: center; font-size: medium;">
             <a href="https://forms.moe.edu.sg/forms/J0lmkJ" target="_blank">
                 &#x1F4DD; Click here to provide feedback on the initial prototype of the Oral Coach &#x1F4DD;
             </a>
         </div>
-    ''',  # Corrected string concatenation
-    css="custom.css"  # Link to the custom CSS file
+    ''',
+    css="custom.css"
 )
 
 iface.queue(max_size=99, concurrency_count=40).launch(debug=True)
-