Added chatting with interviewer

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 
-from llm import get_problem
+from llm import get_problem, send_request
 
 with gr.Blocks() as demo:
     gr.Markdown("Your coding interview practice AI assistant!")
@@ -15,8 +15,13 @@ with gr.Blocks() as demo:
                     "Or keep it blank to just get a random question."
                 ),
             )
+            # TODO: select language
+            # TODO: select difficulty
+            # TODO: select topic
+            # TODO: select LLM model
             start_btn = gr.Button("Start")
         with gr.Accordion("Solution", open=True) as solution_acc:
+            # TODO: auto open close
             with gr.Accordion("Problem description", open=True) as solution_acc:
                 description = gr.Markdown()
             with gr.Row() as content:
@@ -31,6 +36,9 @@ with gr.Blocks() as demo:
             feedback = gr.Markdown()
 
     start_btn.click(fn=get_problem, inputs=requirements, outputs=[description, chat_history], scroll_to_output=True)
+    answer_btn.click(
+        fn=send_request, inputs=[code, previous_code, message, chat_history, chat], outputs=[chat_history, chat, message, previous_code]
+    )
 
 
 demo.launch()

llm.py

@@ -61,3 +61,25 @@ def get_problem(requirements="", client=client):
     chat_history = init_bot(question)
 
     return question, chat_history
+
+
+def send_request(code, previous_code, message, chat_history, chat_display, client=client):
+    if code != previous_code:
+        chat_history.append({"role": "user", "content": f"My latest code: {code}"})
+    chat_history.append({"role": "user", "content": message})
+
+    response = client.chat.completions.create(model="gpt-3.5-turbo", response_format={"type": "json_object"}, messages=chat_history)
+
+    json_reply = response.choices[0].message.content.strip()
+
+    try:
+        data = json.loads(json_reply)
+        reply = data["reply_to_candidate"]
+    except json.JSONDecodeError as e:
+        print("Failed to decode JSON:", str(e))
+
+    chat_history.append({"role": "assistant", "content": json_reply})
+
+    chat_display.append([message, str(reply)])
+
+    return chat_history, chat_display, "", code