clean up dependencies (#1364)

* clean up dependencies

* add requests

Author: adhami3310

.gitignore

@@ -20,4 +20,4 @@ reflex.db
 venv/
 .env
 build
-*.egg-info
+*.egg-info

blog/2024-04-16-custom-components.md

@@ -65,79 +65,6 @@ def zoom():
     )
 ```
 
-## High-Level Components
-
-You can also create custom components that don't wrap React, but instead are built out of existing Reflex components. For example, you can define custom navigation bars, modals, or even entire pages as custom components.
-
-In our [intro chatapp tutorial]({getting_started.chatapp_tutorial.path}) we share the code for creating a chat component in Reflex. Having the full code gives you full flexibility in how your chat component works and appears, but sometimes you just want to drop in a chat component and not worry about the details.
-
-With custom components, we now have a [reflex-chat](https://github.com/picklelo/reflex-chat/) package that you can install with `pip` and use in your app.
-
-```bash
-pip install reflex-chat
-```
-
-You can then import the chat component into your app and use it like any other Reflex component. All the styling and logic is encapsulated, you only need to pass in the actual LLM logic.
-
-```python exec
-import reflex as rx
-from openai import OpenAI
-from reflex_chat import chat
-
-try:
-    client = OpenAI()
-except:
-    client = None
-
-# Only define your logic, the chat component handles the rest.
-async def run_llm(chat_state):
-    # Start a new session to answer the question.
-    session = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=chat_state.get_messages(),
-        stream=True,
-    )
-
-    # Stream the results, yielding after every word.
-    for item in session:
-        if hasattr(item.choices[0].delta, "content"):
-            answer_text = item.choices[0].delta.content
-            # Ensure answer_text is not None before concatenation
-            chat_state.append_to_response(answer_text)
-            yield
-```
-
-```python demo
-rx.box(
-    chat(process=run_llm),
-    height="500px",
-    width="100%"
-)
-```
-
-```md alert info
-# Just set `process` prop of the chat component to connect to your LLM.
-
-\```python
-async def run_llm(chat_state):
-    # Start a new session to answer the question.
-    session = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=chat_state.get_messages(),
-        stream=True,
-    )
-
-    # Stream the results, yielding after every word.
-    for item in session:
-        if hasattr(item.choices[0].delta, "content"):
-            answer_text = item.choices[0].delta.content
-            chat_state.append_to_response(answer_text)
-            yield
-\```
-```
-
-Depending on how much control you want, you can either use a high level component directly or create your own component from scratch.
-
 ### Component State
 
 A new feature we've added in 0.4.6 is the `rx.ComponentState` class. This class allows you to encapsulate state and UI for a component in a single class. In fact, this is what allows the `chat` component to work without having the user having to define a state - the component will dynamically create and handle its own state internally.

pcweb/pages/framework/demos/chatbot/chatbot.py

@@ -107,17 +107,25 @@ def chatbot() -> rx.Component:
     )
 
 
-chatbot_code = """import reflex as rx
-# pip install reflex-chat
-from reflex_chat import chat, api
-
-def index() -> rx.Component:
-    return rx.container(
-        rx.box(
-            chat(process=api.openai()),
-            height="100vh",
+chatbot_code = """
+rx.box(
+    rx.icon_button("trash", on_click=ChatState.clear_chat),
+    rx.box(
+        rx.auto_scroll(
+            rx.foreach(
+                ChatState.chat_history,
+                lambda messages: qa(messages[0], messages[1]),
+            ),
+        ),
+        rx.form(
+            rx.input(
+                placeholder="Ask me anything",
+                name="question",
+            ),
+            rx.icon_button("arrow-up"),
+            on_submit=TutorialState.submit,
+            reset_on_submit=True
         ),
-        size="2",
     )
-
+)
 """

pyproject.toml

@@ -8,25 +8,21 @@ authors = [
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
-  "email-validator==2.1.1",
+  "email-validator==2.2.0",
   "black==23.10.0",
   "pandas>=1.5.3",
-  "psycopg[binary]==3.2.3",
+  "psycopg[binary]==3.2.9",
   "plotly-express==0.4.1",
   "googletrans-py==4.0.0",
-  "typesense==0.14.0",
-  "openai==1.13.3",
-  "pyyaml>=6.0.2",
+  "openai==1.78.1",
   "flexdown>=0.1.5a12,<0.2",
   "reflex @ git+https://github.com/reflex-dev/reflex@main",
   "mistletoe>=1.2.1",
   "reflex-image-zoom>=0.0.2",
-  "reflex-chat==0.0.2a1",
-  "reflex_type_animation==0.0.1",
   "reflex-ag-grid==0.0.10",
-  "replicate==0.32.1",
+  "replicate==1.0.6",
   "reflex-pyplot==0.1.3",
-  "python-multipart==0.0.20",
+  "requests>=2.32.3",
 ]
 
 [dependency-groups]