initial commit

README.md

@@ -1,5 +1,5 @@
 ---
-title: Gradio Chatbot
+title: Gradio 5 Chat
 emoji: 💬
 colorFrom: yellow
 colorTo: purple

app.py

@@ -1,63 +1,33 @@
+# Import necessary libraries
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import numpy as np
+from transformers_js_py import TransformersJS
+
+# Design the chatbot architecture
+class Gradio5Chatbot:
+    def __init__(self):
+        self.model = TransformersJS("t5-small")
+
+    def generate_response(self, input_text):
+        # Implement a retrieval-augmented generation (RAG) system for up-to-date Gradio 5 information
+        # Design a modular structure for easy expansion and maintenance
+        response = self.model.generate(input_text, async_=False)  # Set async_ to False
+        return response
+
+# Implement the core chatbot functionality
+def chatbot(input_text):
+    chatbot = Gradio5Chatbot()
+    response = chatbot.generate_response(input_text)
+    return response
+
+# Create the Gradio 5 interface
+demo = gr.Interface(
+    fn=chatbot,
+    inputs="text",
+    outputs="text",
+    title="Gradio 5 Chatbot",
+    description="A chatbot designed to assist with Gradio 5 development."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the interface
+demo.launch(show_error=True)