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# # Function Calling with OpenAI APIs
import requests
import os
import json
from dotenv import load_dotenv
import streamlit as st
load_dotenv()
from groq import Groq
client = Groq(
api_key=os.getenv("GROQ_API_KEY"),
)
st.title("Weather App with Chat Interface")
input_text = st.text_input("Hi, I am a weather chatbot. Ask me anything!")
if st.button("Ask me"):
if not input_text:
st.error("Please enter a location!")
# ### Define Dummy Function
# Defines a dummy function to get the current weather
def get_current_weather(location):
url = f'https://api.openweathermap.org/data/2.5/weather?q={location}&appid={os.getenv("OPENWEATHER_API_KEY")}'
response = requests.get(url)
data=response.json()
if data['cod'] == 200:
return data
else:
return json.dumps({"city": location, "weather": "Data Fetch Error", "temperature": "N/A"})
# print(get_current_weather("London"))
# ### Define Functions
#
# As demonstrated in the OpenAI documentation, here is a simple example of how to define the functions that are going to be part of the request.
#
# The descriptions are important because these are passed directly to the LLM and the LLM will use the description to determine whether to use the functions or how to use/call.
# # define a function as tools
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
},
]
response = client.chat.completions.create(
model="mixtral-8x7b-32768",
messages=[
{
"role": "user",
"content": input_text,
}
],
temperature=0,
max_tokens=300,
tools=tools,
tool_choice="auto"
)
# print(response)
# print(response.choices[0].message.content)
# print(response['choices'][0]['message']['tool_calls'][0]['function']['arguments'])
groq_response = response.choices[0].message
# print(groq_response)
# response.tool_calls[0].function.arguments
# We can now capture the arguments:
args = json.loads(groq_response.tool_calls[0].function.arguments)
# print(args)
output=get_current_weather(**args)
# print(output)
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
model="mixtral-8x7b-32768",
messages=[
{
"role": "system",
"content": "You are a helpful assistant. You are given the weather details in json format. Read the data and give a brief description of the weather and then answer the question. All temperatures are in kelvin. Only mention details about the weather. "
},
{
"role": "user",
"content": json.dumps(output)
}
],
temperature=0.25,
max_tokens=200,
top_p=1,
stream=True,
stop=None,
)
st.write("Response:")
for chunk in completion:
st.write(chunk.choices[0].delta.content or "", end="")
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