app.py

import streamlit as st
import numpy as np
import torch
import matplotlib.pyplot as plt
import pandas as pd
from groq import Groq
import os

# Load Groq API Key
import os

# Set the environment variable directly in Colab (for this session)
client = Groq(api_key=os.getenv("groq_api_key"))

# Streamlit title
st.title('Smart Energy Load Forecasting')

# File uploader for dataset
uploaded_file = st.file_uploader("Upload a dataset (CSV file)", type=["csv"])

if uploaded_file is not None:
    # Read the uploaded CSV file into a Pandas DataFrame
    df = pd.read_csv(uploaded_file)

    # Show basic information about the dataset
    st.write("Dataset Preview:")
    st.write(df.head())

    # Optionally, display the column names to allow the user to select the relevant columns
    st.write("Columns in the dataset:")
    st.write(df.columns)

    # Allow user to select the column for energy load (assuming the column is named 'energy_load')
    energy_column = st.selectbox("Select the energy load column", df.columns)

    # Display basic statistics of the energy load column
    st.write(f"Statistics for {energy_column}:")
    st.write(df[energy_column].describe())

    # Optionally, display a line chart of the energy load data
    st.write(f"Energy load over time (first 50 records):")
    st.line_chart(df[energy_column].head(50))

# Input for energy prediction
user_input = st.number_input('Enter number of hours for prediction:', min_value=1, max_value=24, value=10)

# GAN Model Setup (Simplified)
class Generator(torch.nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Generator, self).__init__()
        self.fc = torch.nn.Linear(input_dim, output_dim)

    def forward(self, z):
        return torch.tanh(self.fc(z))

# Initialize model
input_dim = 100
output_dim = user_input
generator = Generator(input_dim, output_dim)

# Generate predictions
z = torch.randn(1, input_dim)  # Noise vector
generated_data = generator(z).detach().numpy()

# Display predicted energy load
st.write("Predicted Energy Load (kW) for next {} hours:".format(user_input))
st.line_chart(generated_data[0])

# Use Groq API to interact with model (example call)
chat_completion = client.chat.completions.create(
    messages=[{
        "role": "user",
        "content": f"Predict energy load for the next {user_input} hours."
    }],
    model="llama3-8b-8192",
)

st.write(chat_completion.choices[0].message.content)