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import os
import requests
import gradio as gr
import openai
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
# Define function to get current weather
def get_current_weather(location):
weather_api_key = os.getenv("WEATHER_API_KEY")
base_url = f"http://api.openweathermap.org/data/2.5/weather?q={location}&appid={weather_api_key}&units=metric"
response = requests.get(base_url, timeout=10)
response.raise_for_status()
data = response.json()
return {
"location": location,
"temperature": data['main']['temp'],
"weather": data['weather'][0]['description']
}
# Define chat function
def weather_chat(user_message):
messages = [
{"role": "user", "content": user_message},
{"role": "assistant", "content": "You are a weather bot. Answer only in Celsius. If two cities are asked, provide weather for both."}
]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
temperature=0,
max_tokens=256,
messages=messages,
functions=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "The city, e.g. San Francisco"}
},
"required": ["location"]
}
}
]
)
function_call = response['choices'][0]['message']['function_call']
arguments = eval(function_call['arguments'])
weather_data = get_current_weather(arguments['location'])
messages.append({"role": "assistant", "content": None, "function_call": function_call})
messages.append({"role": "function", "name": "get_current_weather", "content": str(weather_data)})
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages
)
return response['choices'][0]['message']['content']
# Define Gradio interface
iface = gr.Interface(
fn=weather_chat,
inputs=gr.Textbox(label="Weather Queries"),
outputs=gr.Textbox(label="Weather Updates"),
title="DDS Weather Bot",
description="Ask me anything about weather!"
)
# Launch the Gradio interface
iface.launch()
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