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from typing import List
import typing
from aiser import RestAiServer, KnowledgeBase, SemanticSearchResult, Agent
from aiser.models import ChatMessage
import asyncio
import gradio as gr
import requests
import os
# Define environment variables
API_URL = os.getenv("API_URL", "YOUR_API_URL_PLACEHOLDER")
API_TOKEN = os.getenv("API_TOKEN", "YOUR_API_TOKEN_PLACEHOLDER")
class ChatBot:
def __init__(self):
self.history = []
def predict(self, input):
new_user_input = input # User input should be converted into model input format
# Prepare payload for API call
payload = {"question": new_user_input}
# Make an external API call
headers = {"Authorization": API_TOKEN}
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 200:
chat_history_ids = response.json()
else:
chat_history_ids = {"response": "Error in API call"}
# Process the API response and update history
self.history.append(chat_history_ids['response'])
response_text = chat_history_ids['response']
return response_text
bot = ChatBot()
title = "👋🏻Welcome to Tonic's EZ Chat🚀"
description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on [Discord](https://discord.gg/fpEPNZGsbt) to build together."
examples = [["How are you?"]]
iface = gr.Interface(
fn=bot.predict,
title=title,
description=description,
examples=examples,
inputs="text",
outputs="text",
theme="Tonic/indiansummer"
)
iface.launch()
class KnowledgeBaseExample(KnowledgeBase):
def perform_semantic_search(self, query_text: str, desired_number_of_results: int) -> List[SemanticSearchResult]:
result_example = SemanticSearchResult(
content="This is an example of a semantic search result",
score=0.5,
)
return [result_example for _ in range(desired_number_of_results)]
class AgentExample(Agent):
async def reply(self, messages: typing.List[ChatMessage]) -> typing.AsyncGenerator[ChatMessage, None]:
reply_message = "This is an example of a reply from an agent"
for character in reply_message:
yield ChatMessage(text_content=character)
await asyncio.sleep(0.1)
if __name__ == '__main__':
server = RestAiServer(
agents=[
AgentExample(
agent_id='10209b93-2dd0-47a0-8eb2-33fb018a783b' # replace with your agent id
),
],
knowledge_bases=[
KnowledgeBaseExample(
knowledge_base_id='85bc1c72-b8e0-4042-abcf-8eb2d478f207' # replace with your knowledge base id
),
],
port=5000
)
server.run()
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