Create app.py

app.py

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+import os
+from openai import OpenAI
+import gradio as gr
+
+# Set up OpenAI client
+client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+# Initialize conversation history and difficulty
+conversation_history = []
+current_difficulty = "medium"
+
+def get_system_message(difficulty):
+    base_message = """
+You are an AI assistant designed to simulate customer interactions for retail sales training.
+Your goal is to role-play as a customer with a complaint. The human will play the role of a retail sales representative.
+Evaluate the representative's responses and decide whether to continue the conversation or end it.
+If the representative handles the complaint well, respond positively and indicate the conversation is successful.
+If the representative fails to address the concerns adequately, respond negatively and indicate you wish to disengage.
+Start the conversation by presenting a retail-related complaint.
+"""
+    difficulty_adjustments = {
+        "easy": "Be an easy-going customer, open to suggestions and quick to accept reasonable solutions.",
+        "medium": "Be a moderately challenging customer, requiring some convincing but eventually accepting good solutions.",
+        "hard": "Be a difficult customer, skeptical of solutions and requiring exceptional service to be satisfied."
+    }
+    return base_message + difficulty_adjustments[difficulty]
+
+def chat_with_gpt(user_message, history):
+    global conversation_history
+    try:
+        conversation_history.append({"role": "user", "content": user_message})
+        messages = [
+            {"role": "system", "content": get_system_message(current_difficulty)},
+            *conversation_history
+        ]
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            max_tokens=150
+        )
+        bot_response = response.choices[0].message.content.strip()
+        conversation_history.append({"role": "assistant", "content": bot_response})
+        conversation_ended = "conversation ended" in bot_response.lower() or "disengage" in bot_response.lower()
+        history.append((user_message, bot_response))
+        if conversation_ended:
+            summary_feedback = generate_summary_feedback()
+            return history, history, "", summary_feedback
+        return history, history, "", None
+    except Exception as e:
+        error_msg = f"An error occurred in chat_with_gpt: {str(e)}"
+        print(error_msg)
+        return history + [("Error", error_msg)], history + [("Error", error_msg)], "", None
+
+def generate_summary_feedback():
+    try:
+        summary_prompt = "Summarize the conversation, evaluate the sales representative's performance, and provide feedback for improvement. Be concise but comprehensive."
+        messages = [
+            {"role": "system", "content": "You are an AI assistant providing feedback on a retail sales interaction."},
+            *conversation_history,
+            {"role": "user", "content": summary_prompt}
+        ]
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            max_tokens=250
+        )
+        return "Status: Completed\n\n" + response.choices[0].message.content.strip()
+    except Exception as e:
+        return f"Error generating summary: {str(e)}"
+
+def clear_conversation():
+    global conversation_history
+    conversation_history = []
+    return None, None, "", None
+
+def start_new_conversation(difficulty):
+    global current_difficulty
+    current_difficulty = difficulty
+    try:
+        clear_conversation()
+        initial_response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": get_system_message(difficulty)},
+                {"role": "user", "content": f"Start a new {difficulty} retail complaint scenario."}
+            ],
+            max_tokens=150
+        ).choices[0].message.content.strip()
+        conversation_history.append({"role": "assistant", "content": initial_response})
+        return [(None, initial_response)], [(None, initial_response)], "", None
+    except Exception as e:
+        error_msg = f"An error occurred in start_new_conversation: {str(e)}"
+        print(error_msg)
+        return [("Error", error_msg)], [("Error", error_msg)], "", None
+
+def end_conversation(history):
+    global conversation_history
+    conversation_history.append({"role": "user", "content": "The sales representative has ended the conversation."})
+    summary_feedback = generate_summary_feedback()
+    history.append((None, "The sales representative has ended the conversation."))
+    return history, history, "", summary_feedback
+
+# Set up Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Retail Sales Training Simulator")
+    gr.Markdown("Select the difficulty level and start a new scenario to begin.")
+
+    difficulty_radio = gr.Radio(["easy", "medium", "hard"], label="Customer Difficulty", value="medium")
+    new_scenario = gr.Button("Start New Scenario")
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your response")
+    with gr.Row():
+        submit_btn = gr.Button("Submit")
+        end_btn = gr.Button("End Conversation")
+
+    summary = gr.Textbox(label="Conversation Summary and Feedback", lines=10, interactive=False)
+    clear = gr.Button("Clear Conversation")
+
+    new_scenario.click(start_new_conversation, inputs=[difficulty_radio], outputs=[chatbot, chatbot, msg, summary])
+    submit_btn.click(chat_with_gpt, [msg, chatbot], [chatbot, chatbot, msg, summary])
+    msg.submit(chat_with_gpt, [msg, chatbot], [chatbot, chatbot, msg, summary])
+    end_btn.click(end_conversation, inputs=[chatbot], outputs=[chatbot, chatbot, msg, summary])
+    clear.click(clear_conversation, inputs=None, outputs=[chatbot, msg, msg, summary])
+
+# Launch the interface
+demo.launch()