Update app.py

app.py

@@ -1,8 +1,8 @@
-#app.py
+# app.py
 import gradio as gr
 import openai
 import os
-import HongWenData  # Importing the HongWenData module
+import RadinMas  # Importing the RadinMas module
 import base64
 
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
@@ -12,9 +12,9 @@ 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("HongWenSBC.JPG")
+img_base64 = image_to_base64("RadinMasSBC.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, audio):
     # Transcribe the audio using Whisper
     with open(audio, "rb") as audio_file:
@@ -22,16 +22,16 @@ def predict(question_choice, audio):
     message = transcript["text"]  # This is the transcribed message from the audio input
     
     # Generate the system message based on the chosen question
-    strategy, explanation = HongWenData.strategy_text["TREES"]
+    system_message = RadinMas.generate_system_message(question_choice)
     
-    # Reference to the picture description from HongWenData.py
-    picture_description = HongWenData.description
+    # Reference to the picture description from RadinMas.py
+    picture_description = RadinMas.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 == HongWenData.questions[0] else ""
+    """ if question_choice == RadinMas.questions[0] else ""
 
     # Construct the conversation with the system and user's message
     conversation = [
@@ -41,22 +41,17 @@ def predict(question_choice, audio):
             You are an expert English Language Teacher in a Singapore Primary school, directly guiding a Primary 6 student in Singapore. 
             The student is answering the question: '{question_choice}'. 
             {picture_description_inclusion}
-            Point out areas they did well and where they can improve, following the {strategy}. 
-            Encourage the use of sophisticated vocabulary and expressions.
-            For the second and third questions, the picture is not relevant, so the student should not refer to it in their response.
-            {explanation}
-            The feedback should be in second person, addressing the student directly.
+            {system_message}
             """
         },
         {"role": "user", "content": message}
     ]
 
-
     response = openai.ChatCompletion.create(
         model='gpt-3.5-turbo',
         messages=conversation,
         temperature=0.6,
-        max_tokens=1000,  # Limiting the response to 1000 tokens
+        max_tokens=500,  # Limiting the response to 500 tokens
         stream=True
     )
 
@@ -70,7 +65,7 @@ def predict(question_choice, audio):
 iface = gr.Interface(
     fn=predict,
     inputs=[
-        gr.Radio(HongWenData.questions, label="Choose a question", default=HongWenData.questions[0]),  # Dropdown for question choice
+        gr.Radio(RadinMas.questions, label="Choose a question", default=RadinMas.questions[0]),  # Dropdown for question choice
         gr.inputs.Audio(source="microphone", type="filepath")  # Audio input
     ],
     outputs=gr.inputs.Textbox(),  # Using inputs.Textbox as an output to make it editable