Create app.py

app.py

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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="")