Update app.py

app.py

@@ -2,7 +2,7 @@
 import gradio as gr
 import openai
 import os
-import HongWenData  # Importing the HongWenData module
+import RiverValleyData  # Importing the RiverValleyData 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("HongWenSBC.JPG")
+img_base64 = image_to_base64("RiverValleySBC.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):
@@ -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"]
+    strategy, explanation = RiverValleyData.strategy_text["SEP"]
     
-    # Reference to the picture description from HongWenData.py
-    picture_description = HongWenData.description
+    # Reference to the picture description from RiverValleyData.py
+    picture_description = RiverValleyData.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 == RiverValleyData.questions[0] else ""
 
     # Construct the conversation with the system and user's message
     conversation = [
@@ -51,12 +51,11 @@ def predict(question_choice, audio):
         {"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 +69,7 @@ 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(RiverValleyData.questions, label="Choose a question", default=RiverValleyData.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
@@ -81,4 +80,3 @@ iface = gr.Interface(
 iface.queue(max_size=99, concurrency_count=40).launch(debug=True)
 
 
-