app.py

# # 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="")