Upload 2 files

app.py

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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).
+""")

requirements.txt

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+pandas
+numpy
+networkx
+matplotlib
+plotly
+faiss-cpu
+groq
+openpyxl