# # 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"] # } # } # }, # ] def get_response(input_text): client = Groq( api_key=os.getenv("GROQ_API_KEY"), ) 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, ) output="" # st.write("Response:") for chunk in completion: output+=chunk.choices[0].delta.content or "" # output+="\n" return output def main(): st.title("Weather Chatbot") # User input st.write("Hi, I am a weather chatbot. Ask me anything!") location = st.text_input("Type in your question") # Ask me button if st.button("Ask me"): # Check if location is provided if location: # Get current weather response = get_response(location) # Display weather details st.json(response) else: st.warning("Please enter a city name.") if __name__ == "__main__": main()