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import streamlit as st
import os
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from groq import Groq
from io import BytesIO
GROQ_API_KEY = "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" # Replace with your Groq API key
client = Groq(api_key=GROQ_API_KEY)
# Function to upload the dataset
def upload_file():
uploaded_file = st.file_uploader("Upload your Excel dataset", type=["xlsx"])
if uploaded_file is not None:
return pd.read_excel(uploaded_file)
return None
# Function to construct the graph and visualize
def construct_graph(dataset):
G = nx.DiGraph()
# Add nodes and edges based on the dataset
for _, row in dataset.iterrows():
# Replace with your column names if necessary
G.add_edge(row['Vendor Name'], row['Meter Number'], weight=row['Consumption (HCF)'])
# Visualize the graph (water distribution network)
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G) # Layout for visualization
nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=1500, edge_color='gray')
# Render the graph as an image and return it
buf = BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
return buf
# Function to query Groq for optimization suggestions based on serial number
def query_groq(serial_number):
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": f"Optimize water distribution network for Serial Number {serial_number} to minimize consumption fluctuations."
}
],
model="llama3-8b-8192",
)
return chat_completion.choices[0].message.content
# Streamlit UI
st.set_page_config(page_title="Water Distribution Network Optimization", page_icon="💧")
# Title and description
st.title("💧 Intelligent Water Distribution Network Optimization")
st.write("Upload your water distribution dataset and optimize the network based on specific serial numbers.")
# Step 1: Upload file
dataset = upload_file()
if dataset is not None:
# Show uploaded dataset details
st.subheader("Dataset Preview:")
st.write(dataset.head())
# Check if there's a serial number column, add if missing
if 'Serial Number' not in dataset.columns:
dataset['Serial Number'] = range(1, len(dataset) + 1) # Adding Serial Number column
# Step 2: Select Serial Number for analysis
serial_number = st.number_input("Enter Serial Number for analysis", min_value=1, max_value=int(dataset['Serial Number'].max()), step=1)
if serial_number in dataset['Serial Number'].values:
# Filter the dataset for the selected serial number
filtered_data = dataset[dataset['Serial Number'] == serial_number]
st.write(f"Filtered data for Serial Number {serial_number}:", filtered_data)
# Step 3: Display the graph
st.subheader(f"Graph for Serial Number {serial_number}")
graph_image = construct_graph(filtered_data)
st.image(graph_image, caption=f"Water Distribution Network for Serial Number {serial_number}", use_column_width=True)
# Step 4: Query Groq for optimization
if st.button(f"Optimize for Serial Number {serial_number}"):
optimization_response = query_groq(serial_number)
st.subheader("Optimization Suggestions:")
st.write(optimization_response)
else:
st.info("Please upload a dataset to get started.")
# Footer
st.markdown("""
---
Developed by [Your Name].
For more information, visit our [website](https://example.com).
""")
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