sensor_app.py

import streamlit as st
import pandas as pd
import numpy as np
import datetime
import collections
import openai
#import os
#from dotenv import load_dotenv

#load_dotenv('env/data.env')

# Initialize OpenAI API
openai.api_key = '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'


st.title('Sein Farm in your hand')
tab1, tab2, tab3= st.tabs(['Tab A' , 'Tab B', 'Tab C'])

with tab1:
    st.header('About Sein Farm')


with tab2:
    st.write('Displaying dummy data for temperature and pH levels.')

    col1,col2 = st.columns([1,1])

    # Generate dummy data
    def generate_data():
        time_range = pd.date_range(start="now", periods=60, freq="T")  # Every minute for an hour
        temperature = np.random.uniform(20, 30, 60)  # Random temperature values between 20 and 30
        pH = np.random.uniform(6.5, 7.5, 60)  # Random pH values between 6.5 and 7.5
        return pd.DataFrame({
            "Time": time_range,
            "Temperature": temperature,
            "pH": pH
        })
    data = generate_data()

    with col1 :
        st.subheader('Temperature over Time')
        st.line_chart(data.set_index('Time')['Temperature'])

    
    with col2 :
        st.subheader('pH Level over Time')
        st.line_chart(data.set_index('Time')['pH'])

with tab3:
        # Mock GPT-based API
    def get_gpt_response(message, context):
        # In a real scenario, this function would call the GPT API and get a response.
        # For this example, if the message contains "temperature", we return the latest temperature.
        if "temperature" in message.lower():
            return f"The latest temperature reading is {context['Temperature']}°C."
        elif "ph" in message.lower():
            return f"The latest pH reading is {context['pH']}."
        else:
            return "I'm sorry, I don't understand that."



    avg_temperature = data['Temperature'].mean()
    min_temperature = data['Temperature'].min()
    max_temperature = data['Temperature'].max()

    avg_pH = data['pH'].mean()
    min_pH = data['pH'].min()
    max_pH = data['pH'].max()

    # Define the optimal conditions for raising fish
    optimal_conditions = """
    The optimal conditions for raising fish are a pH level between 6.5 and 7.5, and a temperature between 20°C and 25°C.
    """

    # Create the context message

    system_message = f"""
    I am your AI secretary. Based on the data:
    - Average temperature: {avg_temperature:.2f}°C
    - Average pH level: {avg_pH:.2f}
    - Lowest pH level: {min_pH:.2f}
    - Highest pH level: {max_pH:.2f}
    {optimal_conditions}
    Given this information, how can I assist you?
    """


    # Chatbot interface
    user_message = st.text_input("Ask the AI Secretary:")


    if not user_message:
        user_message = "Given this information, what would be your advice?"


    # User's message
    prompt = user_message

    # Response from the API
    completion = openai.ChatCompletion.create(model="gpt-3.5-turbo",
                                            messages=[
                                                {"role": "system", "content": system_message},
                                                {"role": "user", "content": prompt}
                                            ])

    # Extract the response
    response = completion.choices[0].message.content
    st.write(f"AI Secretary: {response}")