[HOS-701] update chat app tutorial docs (#1350)

* update chat app tutorail docs

* update chat tutorial

* trim sections

---------

Co-authored-by: pourhakimi <[email protected]>

Author: LineIndent

docs/getting_started/chatapp_tutorial.md

@@ -437,7 +437,7 @@ rx.container(
 ```python
 # chatapp.py
 from chatapp.state import State
-...
+
 
 def chat() -> rx.Component:
     return rx.box(
@@ -447,7 +447,7 @@ def chat() -> rx.Component:
         )
     )
 
-...
+
 
 def action_bar() -> rx.Component:
     return rx.hstack(
@@ -499,7 +499,6 @@ def action_bar() -> rx.Component:
 
 ```python
 # state.py
-
 @rx.event
 def answer(self):
     # Our chatbot is not very smart right now...
@@ -536,7 +535,6 @@ rx.container(
 # state.py
 import asyncio
 
-...
 async def answer(self):
     # Our chatbot is not very smart right now...
     answer = "I don't know!"
@@ -565,60 +563,51 @@ We will use OpenAI's API to give our chatbot some intelligence.
 
 ### Configure the OpenAI API Key
 
-Ensure you have an active OpenAI subscription. Save your API key as an environment variable named `OPENAI_API_KEY`:
+First, ensure you have an active OpenAI subscription.
+Next, install the latest openai package:
+```bash 
+pip install --upgrade openai
+```
 
-    ```bash
-    export OPENAI_API_KEY="your-api-key-here"
-    ```
+Direct Configuration of API in Code
 
-Install the `openai` pypi package:
+Update the state.py file to include your API key directly:
 
-    ```bash
-    pip install openai
-    ```
+```python
+# state.py
+import os
+from openai import AsyncOpenAI
 
-### Using the API
+import reflex as rx
 
-We need to modify our event handler to send a request to the API.
+# Initialize the OpenAI client
+client = AsyncOpenAI(api_key="YOUR_OPENAI_API_KEY")  # Replace with your actual API key
 
-```python exec
-def qa(question: str, answer: str) -> rx.Component:
-    return rx.box(
-        rx.box(rx.text(question, style=style.question_style), text_align="right"),
-        rx.box(rx.text(answer, style=style.answer_style), text_align="left"),
-        margin_y="1em",
-    )
+```
 
+### Using the API
 
-def chat1() -> rx.Component:
-    return rx.box(
-        rx.foreach(
-            ChatappState.chat_history, lambda messages: qa(messages[0], messages[1])
-        )
-    )
+Making your chatbot intelligent requires connecting to a language model API. This section explains how to integrate with OpenAI's API to power your chatbot's responses.
 
+1. First, the user types a prompt that is updated via the `on_change` event handler.
+2. Next, when a prompt is ready, the user can choose to submit it by clicking the `Ask` button which in turn triggers the `State.answer` method inside our `state.py` file.
+3. Finally, if the method is triggered, the `prompt` is sent via a request to OpenAI client and returns an answer that we can trim and use to update the chat history! 
 
-def action_bar3() -> rx.Component:
+
+```python
+# chatapp.py
+def action_bar() -> rx.Component:
     return rx.hstack(
         rx.input(
-            value=ChatappState.question,
+            value=State.question,
             placeholder="Ask a question",
-            on_change=ChatappState.set_question,
-            style=style.input_style,
-        ),
-        rx.button("Ask", on_click=ChatappState.answer4, style=style.button_style),
-    )
-```
+            # on_change event updates the input as the user types a prompt.
+            on_change=State.set_question,
+            style=style.input_style),
 
-```python demo box
-rx.center(
-    rx.vstack(
-        chat1(),
-        action_bar3(),
-        align="center",
-        width="100%",
+        # on_click event triggers the API to send the prompt to OpenAI.
+        rx.button("Ask", on_click=State.answer, style=style.button_style),
     )
-)
 ```
 
 ```python
@@ -665,20 +654,36 @@ Finally, we have our chatbot!
 
 ### Final Code
 
-We wrote all our code in three files, which you can find below.
+This application is a simple, interactive chatbot built with Reflex that leverages OpenAI's API for intelligent responses. The chatbot features a clean interface with streaming responses for a natural conversation experience.
+
+Key Features
+
+1. Real-time streaming responses
+2. Clean, visually distinct chat bubbles for questions and answers
+3. Simple input interface with question field and submit button
+
+Project Structure
+
+Below is the full chatbot code with a commented title that corresponds to the filename.
+
+```text
+chatapp/
+├── chatapp.py    # UI components and app setup
+├── state.py      # State management and API integration
+└── style.py      # Styling definitions
+```
+
+The `chatapp.py` file:
 
 ```python
-# chatapp.py
 import reflex as rx
-
 from chatapp import style
 from chatapp.state import State
 
-
 def qa(question: str, answer: str) -> rx.Component:
     return rx.box(
-        rx.box(rx.text(question, style=style.question_style),text_align="right"),
-        rx.box(rx.text(answer, style=style.answer_style),text_align="left"),
+        rx.box(rx.text(question, style=style.question_style), text_align="right"),
+        rx.box(rx.text(answer, style=style.answer_style), text_align="left"),
         margin_y="1em",
     )
 
@@ -690,7 +695,6 @@ def chat() -> rx.Component:
         )
     )
 
-
 def action_bar() -> rx.Component:
     return rx.hstack(
         rx.input(
@@ -706,7 +710,6 @@ def action_bar() -> rx.Component:
         ),
     )
 
-
 def index() -> rx.Component:
     return rx.center(
         rx.vstack(
@@ -716,66 +719,58 @@ def index() -> rx.Component:
         )
     )
 
-
 app = rx.App()
 app.add_page(index)
 ```
 
+
+The `state.py` file:
+
 ```python
-# state.py
 import os
-
 from openai import AsyncOpenAI
-
 import reflex as rx
 
 class State(rx.State):
-
-    # The current question being asked.
     question: str
-
-    # Keep track of the chat history as a list of (question, answer) tuples.
-    chat_history: list[tuple[str, str]]
+    chat_history: list[tuple[str, str]] = []
 
     async def answer(self):
-        # Our chatbot has some brains now!
         client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])
-
+        
+        # Start streaming completion from OpenAI
         session = await client.chat.completions.create(
             model="gpt-4o-mini",
-            messages=[\{"role": "user", "content": self.question}],
-            stop=None,
+            messages=[
+                \{"role": "user", "content": self.question}
+            ],    
             temperature=0.7,
             stream=True,
         )
 
-        # Add to the answer as the chatbot responds.
+        # Initialize response and update UI
         answer = ""
         self.chat_history.append((self.question, answer))
-
-        # Clear the question input.
         self.question = ""
-        # Yield here to clear the frontend input before continuing.
         yield
-
+        
+        # Process streaming response
         async for item in session:
             if hasattr(item.choices[0].delta, "content"):
                 if item.choices[0].delta.content is None:
-                    # presence of 'None' indicates the end of the response
                     break
                 answer += item.choices[0].delta.content
-                self.chat_history[-1] = (
-                    self.chat_history[-1][0],
-                    answer,
-                )
+                self.chat_history[-1] = (self.chat_history[-1][0], answer)
                 yield
 ```
 
+
+The `style.py` file:
+
 ```python
-# style.py
 import reflex as rx
 
-# Common styles for questions and answers.
+# Common style base
 shadow = "rgba(0, 0, 0, 0.15) 0px 2px 8px"
 chat_margin = "20%"
 message_style = dict(
@@ -787,7 +782,7 @@ message_style = dict(
     display="inline-block",
 )
 
-# Set specific styles for questions and answers.
+# Styles for questions and answers
 question_style = message_style | dict(
     margin_left=chat_margin,
     background_color=rx.color("gray", 4),
@@ -797,18 +792,16 @@ answer_style = message_style | dict(
     background_color=rx.color("accent", 8),
 )
 
-# Styles for the action bar.
+# Styles for input elements
 input_style = dict(border_width="1px", padding="0.5em", box_shadow=shadow, width="350px")
-button_style = dict(
-    background_color=rx.color("accent", 10), box_shadow=shadow
-)
-
+button_style = dict(background_color=rx.color("accent", 10), box_shadow=shadow)
 ```
 
+
 ### Next Steps
 
 Congratulations! You have built your first chatbot. From here, you can read through the rest of the documentations to learn about Reflex in more detail. The best way to learn is to build something, so try to build your own app using this as a starting point!
 
 ### One More Thing
 
-With our hosting service, you can deploy this app with a single command within minutes. Check out our [Hosting Quick Start]({hosting.deploy_quick_start.path}).
+With our hosting service, you can deploy this app with a single command within minutes. Check out our [Hosting Quick Start]({hosting.deploy_quick_start.path}).