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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 | |
import logging | |
# Configure logging | |
logging.basicConfig(level=logging.DEBUG) | |
# Define environment variables | |
API_URL = os.getenv("API_URL", "http://ec2-54-166-81-166.compute-1.amazonaws.com:3000/api/v1/prediction/117a5076-c05e-4208-91d9-d0e772bf981e") | |
API_TOKEN = os.getenv("API_TOKEN", "Bearer 0Ouk5cgljCYuuF3LDfBkIAcuqj9hgWaaK5qRCLfbfrg=") | |
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) | |
# Initialize the response text with an error message by default | |
response_text = f"API call failed with status code {response.status_code}" | |
if response.status_code == 200: | |
response_text = response.text # Get the raw text response | |
# Process the API response and update history | |
self.history.append(response_text) | |
# Log API request and response | |
logging.debug(f"API Request: {API_URL}, Payload: {payload}, Headers: {headers}") | |
logging.debug(f"API Response: {response.status_code}, Content: {response_text}") | |
# Return the response text | |
return response_text | |
bot = ChatBot() | |
title = "👋🏻Welcome to Conversate with Bible Scriptures🌠" | |
description = "Here you can ask questions about bible scriptures or your faith & life" | |
examples = ["What does the bible say about the value of hard work?"] | |
iface = gr.Interface( | |
fn=bot.predict, | |
title=title, | |
description=description, | |
examples=examples, | |
inputs="text", | |
outputs="text") | |
iface.launch() | |
# Placeholder classes, replace with actual implementations | |
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() |