update name

README.md

@@ -1,5 +1,5 @@
 ---
-title: EvaLingo
+title: KAKAPO EFL Chatbot
 app_file: app.py
 sdk: gradio
 sdk_version: 4.23.0

app.py

@@ -34,8 +34,6 @@ headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 # Global variable to control debug printing
 DEBUG_MODE = True
 
-
-
 try:
     file_path = "issun-boshi.txt"
     # Open the file in read mode ('r')
@@ -54,8 +52,6 @@ def debug_print(*args, **kwargs):
     if DEBUG_MODE:
         print(*args, **kwargs)
 
-
-
 def translate_openai(input_text):
 
     prompt = "Translate the following text into Japanese language: " + input_text
@@ -139,32 +135,52 @@ def predict(message, history):
         history_openai_format.append({"role": "assistant", "content":assistant})
     history_openai_format.append({"role": "user", "content": message})
   
-    response = client.chat.completions.create(
-        model='gpt-3.5-turbo',
-        messages=history_openai_format,
-        temperature=1.0, #?
-        #max_tokens=150,
-        stream=True
-    )
+    try:
+        response = client.chat.completions.create(
+            model='gpt-3.5-turbo',
+            messages=history_openai_format,
+            temperature=0.7,
+            #max_tokens=150,
+            stream=True
+        )
+    except Exception as e:
+        debug_print("Error in getting LLM response.", str(e))
+    try:
+        partial_message = ""
+        for chunk in response:
+            if chunk.choices[0].delta.content is not None:
+                partial_message = partial_message + chunk.choices[0].delta.content
+                yield partial_message
+
+    except Exception as e:
+        debug_print("Error in streaming output", str(e))
 
-    partial_message = ""
-    for chunk in response:
-        if chunk.choices[0].delta.content is not None:
-              partial_message = partial_message + chunk.choices[0].delta.content
-              yield partial_message
+strategies = '''making connections between the text and their prior knowledge;
+forming and testing hypotheses about texts;
+asking questions about  the text;
+creating mental images or visualising;
+inferring meaning from  the text;
+identifying the writer’s purpose and point of view;
+identifying the main idea or theme in the text;
+summarising the information or events in  the text;
+analysing and synthesising ideas, information, structures, and features in  the text;
+evaluating ideas and information'''
 
 
 with gr.Blocks() as demo:
     with gr.Row():
-        learner_data = gr.Textbox(label="Learner Data", placeholder="Enter learner data here...", lines=4, value="Honoka is a Japanese EFL student.")
+        learner_data = gr.Textbox(label="Learner Data", placeholder="Enter learner data here...", lines=4, value="Honoka is a Japanese EFL student. [summary of relevant student data]")
         learning_content = gr.Textbox(label="Learning Content", placeholder="Enter learning content here...", lines=4, value=learning_content)
         teacher_prompt = gr.Textbox(label="Teacher Prompt", placeholder="Enter chat guidance here...", lines=4, 
-                                    value="You are a professional EFL teacher. Guide the conversation to discuss the Learning Content below.")
+                                    value=f"You are a professional EFL teacher. Help the student actively read the text using these strategies: {strategies}. Guide the conversation to discuss the Learning Content below.")
         
         # pre prompt the history_openai_format list
         history_openai_format.append({"role": "system", "content": f"{teacher_prompt.value} Learner Data: {learner_data.value}. Learning Content: {learning_content.value}. "})
 
-    gr.ChatInterface(predict)
+    try:
+        gr.ChatInterface(predict)
+    except Exception as e:
+        debug_print("Error in gr.ChatInterface(predict)", str(e))
 
 demo.launch(debug=True)