redo as simple blocks app with no export yet

app.py

@@ -2,17 +2,11 @@
 
 # openai: used to communicate with the OpenAI API for generating model responses.
 # gradio: provides a simple way to create user interfaces for models.
-# json: allows us to work with JSON data.
-# csv: enables working with CSV files.
-# fpdf: a library to generate PDF files.
 
-#! pip install -q openai gradio fpdf
+#! pip install -q openai gradio
 
 import openai
 import gradio as gr
-import json
-import csv
-from fpdf import FPDF
 
 # Initializing system message which provides initial context and instructions for the model.
 sys_message =  """You are a helpful and friendly coach helping a graduate student reflect on their recent class experience in Advanced Corporate Valuation at Vanderbilt's Owen Graduate School of Management.
@@ -43,7 +37,6 @@ Wrap up the conversation by praising reflective thinking. Let the student know w
 their reflections are especially thoughtful or demonstrate progress. Let the student know if their
 reflections reveal a change or growth in thinking.
 """
-des = "This chatbot will help you write your final reflection for Advanced Corp Val. Start by saying Hi!"
 
 # Function Definitions
 
@@ -53,51 +46,6 @@ des = "This chatbot will help you write your final reflection for Advanced Corp
 # It manages the conversational context/history and ensures that the
 # conversation flows naturally.
 
-# 2. combined_function:
-# This function handles the logic for downloading the chat history in different formats.
-# Based on the user's choice (JSON, CSV, PDF), it calls the appropriate function
-# to convert the chat history and returns a download link.
-
-# 3. save_to_json, save_to_csv, save_to_pdf:
-# These helper functions take the chat history as input and save it to the
-# respective file formats.
-
-# Let's integrate the extracted function definitions into our documented content.
-def convert_chat_history(format):
-    if format == "json":
-        file_path = "chat_history.json"
-        with open(file_path, 'w') as file:
-            json.dump(chat_log, file)
-    elif format == "csv":
-        file_path = "chat_history.csv"
-        with open(file_path, 'w', newline='') as file:
-            writer = csv.DictWriter(file, fieldnames=["role", "content"])
-            writer.writeheader()
-            for row in chat_log:
-                writer.writerow(row)
-    elif format == "pdf":
-        file_path = "chat_history.pdf"
-        pdf = FPDF()
-        pdf.add_page()
-        pdf.set_font("Arial", size=12)
-        for entry in chat_log:
-            pdf.cell(200, 10, txt="{}: {}".format(entry["role"].title(), entry["content"]), ln=True)
-        pdf.output(file_path)
-    else:
-        return None
-    return file_path
-
-def download_json():
-    convert_chat_history("json")
-    return "Downloaded JSON"
-
-def download_csv():
-    convert_chat_history("csv")
-    return "Downloaded CSV"
-
-def download_pdf():
-    convert_chat_history("pdf")
-    return "Downloaded PDF"
 
 def CustomChatGPT(message, history):
     history_openai_format = [{"role": "system", "content": sys_message}]
@@ -119,58 +67,24 @@ def CustomChatGPT(message, history):
             partial_message = partial_message + chunk['choices'][0]['delta']['content']
             yield partial_message
 
-chat_ui = gr.ChatInterface(fn=CustomChatGPT,
-                     theme = 'gradio/soft',
-                     title = "Advanced Corp Val AI Coach",
-                    description = des)
-
-def combined_function(download_format):
-    if download_format == "json":
-        download_json(history_openai_format)
-        return "JSON downloaded", ""
-    elif download_format == "csv":
-        download_csv(history_openai_format)
-        return "CSV downloaded", ""
-    elif download_format == "pdf":
-        download_pdf(history_openai_format)
-        return "PDF downloaded", ""
-    else:
-        return CustomChatGPT(history_openai_format), ""
-
-
-
-download_ui = gr.Interface(
-    fn=combined_function,
-    inputs=[gr.Radio(choices = ["json", "csv", "pdf"], label="Download Options")],
-    outputs=gr.Textbox(label = "Download status", placeholder = "Downloading..."),
-    live=True,
-    layout="vertical",
-    theme="huggingface",
-    server_name="0.0.0.0",
-    server_port=7860,
-    allow_flagging=False
-)
-
-# Gradio Interface Creation
-
-# Two separate Gradio interfaces are created:
-
-# 1. chat_ui:
-# An interface for the chatbot, where users can interact with the AI model
-# in a conversational format. It uses the CustomChatGPT function to process
-# user inputs and generate model responses.
-
-# 2. download_ui:
-# An interface that allows users to download the chat history in different
-# formats (JSON, CSV, PDF). The combined_function manages the logic for
-# this interface.
-
-# The main execution section of the code initializes these interfaces and
-# launches them in parallel, allowing users to switch between the chatbot
-# and download interfaces.
-
-demo = gr.Parallel(chat_ui, download_ui)
-#demo = gr.Parallel(chat_ui)
-
-if __name__ == "__main__":
-    demo.launch()
+
+
+
+with gr.Blocks() as demo:
+  gr.Markdown("# Advanced Corp Val AI Coach")
+  gr.Markdown("## I am your Advanced Corp Val AI Coach. I'm here to help you with your final reflection on this course. Start by saying hi!")
+
+# Redesigned chatbot using Blocks (reference: https://www.geeksforgeeks.org/create-a-chatbot-with-openai-and-gradio-in-python/)
+  with gr.Blocks():
+    chatbot = gr.Chatbot()
+    with gr.Row(equal_height = True):
+      message = gr.Textbox(placeholder="Type your questioins here!")
+      state = gr.State()
+      submit = gr.Button("Send message")
+      clear = gr.ClearButton([message, chatbot])
+      submit.click(message_and_history,
+                inputs=[message, state],
+                outputs=[chatbot, state])
+
+  demo.queue()
+  demo.launch(debug = True)