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First ever gradio template
This video demonstrates how to use the First ever gradio template template.
import logging
import gradio as gr
from flask import Flask, render_template, session, request, jsonify
from flask_session import Session
from gunicorn.app.base import BaseApplication
from abilities import llm
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = Flask(__name__)
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)
def chatbot(message, history, model, temperature):
conversation_history = " ".join([f"user: {h[0]} bot: {h[1]}" for h in history])
conversation_history += f" user: {message}"
response = llm(
prompt=conversation_history,
response_schema={
"type": "object",
"properties": {Frequently Asked Questions
What are some potential business applications for this First ever gradio template?
The First ever gradio template offers versatile applications across various industries. It can be used for customer support chatbots, virtual assistants in e-commerce, automated FAQ systems for businesses, or even as an internal tool for employee queries. The template's flexibility allows it to be customized for specific business needs, making it valuable for companies looking to implement AI-driven conversational interfaces.
How can this template improve customer engagement for businesses?
This First ever gradio template can significantly enhance customer engagement by providing instant, 24/7 responses to customer queries. It allows businesses to offer personalized interactions at scale, reducing wait times and improving overall customer satisfaction. The ability to choose different AI models and adjust temperature settings enables businesses to fine-tune the chatbot's responses to match their brand voice and communication style.
What are the cost implications of implementing this template for a small business?
Implementing the First ever gradio template can be cost-effective for small businesses. It reduces the need for extensive customer support staff while providing round-the-clock service. The template's use of open-source libraries like Flask and Gradio keeps initial setup costs low. However, businesses should consider the ongoing costs of API calls to the chosen AI model, which can vary based on usage and the specific model selected.
How can I customize the AI models available in the dropdown menu?
To customize the AI models in the dropdown menu, you can modify the model variable in the launch() function. Here's an example of how to add or remove models:
python
model = gr.Dropdown(
["gpt-4o", "gpt-3.5-turbo", "claude-2", "custom-model"],
label="AI Model",
value="gpt-4o"
)
Make sure to also update the llm() function in the abilities.py file to handle the new models you've added.
How can I add a feature to save conversation history?
To add a feature for saving conversation history in the First ever gradio template, you can modify the chatbot() function to store conversations in a database or file. Here's a simple example using a JSON file:
```python import json from datetime import datetime
def save_conversation(history, user_id): conversation = { "user_id": user_id, "timestamp": datetime.now().isoformat(), "history": history } with open("conversation_history.json", "a") as f: json.dump(conversation, f) f.write("\n")
def chatbot(message, history, model, temperature): # Existing code... response = llm(...) save_conversation(history + [[message, response['response']]], "user123") return response['response'] ```
This code snippet saves each conversation with a timestamp and user ID. Remember to handle user authentication and ID generation in a production environment.
Created: | Last Updated:
Here's a step-by-step guide on how to use the provided Gradio chatbot template:
Introduction to the Template
This template provides a customizable chatbot interface using Gradio, Flask, and various AI models. It allows users to interact with different language models through a simple web interface.
Getting Started
- Click "Start with this Template" to begin using the chatbot template in the Lazy Builder interface.
Test the Application
- Press the "Test" button to deploy the application and launch the Lazy CLI.
Using the Chatbot
-
Once the application is deployed, you'll receive a dedicated server link to access the chatbot interface.
-
Open the provided link in your web browser to interact with the chatbot.
-
The chatbot interface includes the following elements:
- A chat window displaying the conversation history
- A text input box for entering your messages
- A dropdown menu to select the AI model
-
A temperature slider to adjust the model's creativity
-
To use the chatbot:
- Type your message in the text input box
- Select an AI model from the dropdown menu (options include gpt-4o, gpt-4o-mini, claude-3-5-sonnet, claude-3-haiku, and deepseek/deepseek-coder)
- Adjust the temperature slider if desired (higher values increase randomness)
-
Press Enter or click the submit button to send your message
-
The chatbot will process your input and generate a response based on the selected model and settings.
-
Continue the conversation by sending additional messages and receiving responses from the AI.
Customizing the Chatbot
The template allows for easy customization:
- To add or remove AI models, modify the
modeldropdown options in thelaunch()function. - To adjust the temperature range, modify the
temperatureslider parameters in thelaunch()function. - To change the chatbot's behavior or add new features, you can modify the
chatbot()function in the code.
Remember that all changes and deployments are handled through the Lazy platform, so you don't need to worry about setting up a local environment or managing dependencies.
Template Benefits
-
Rapid Prototyping: This template allows businesses to quickly set up and deploy AI-powered chatbot interfaces, enabling fast prototyping and testing of conversational AI applications.
-
Flexible Model Selection: With the ability to choose from multiple AI models (e.g., GPT-4, Claude, DeepSeek), businesses can easily experiment with different language models to find the best fit for their specific use case.
-
Customizable User Experience: The Gradio interface provides a user-friendly chat interface that can be easily customized and integrated into existing web applications, improving customer engagement and interaction.
-
Scalable Architecture: The use of Flask and Gunicorn allows for easy scaling of the application to handle increased user loads, making it suitable for both small-scale testing and large-scale deployments.
-
Cost-Effective Development: By leveraging open-source tools and providing a ready-to-use template, businesses can significantly reduce development time and costs associated with building conversational AI interfaces from scratch.
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