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Update app.py
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app.py
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import os
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import streamlit as st
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import
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st.
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)
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#
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if
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#
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# Back Button to Redirect to Dashboard
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st.markdown(
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"""
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<style>
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.back-button {
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display: flex;
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justify-content: center;
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padding: 10px;
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}
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.btn {
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background-color: #4CAF50;
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color: white;
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padding: 10px 20px;
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text-align: center;
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text-decoration: none;
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font-size: 16px;
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border-radius: 5px;
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display: inline-block;
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}
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</style>
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<div class="back-button">
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<a href="https://binsight.onrender.com/dashboard.html" class="btn">🔙 Back to Dashboard</a>
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</div>
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""",
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unsafe_allow_html=True,
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)
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# Introduction and instructions
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st.markdown(
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"""
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Welcome to the **Smart Waste Management System**! This tool helps **citizens, municipal workers,
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recycling companies, and biogas plants** collaborate efficiently for **better waste management**.
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### **🌟 Key Features**
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- 🏡 **Improve waste collection efficiency** for citizens.
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- 🚛 **Help municipal workers** manage schedules.
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- 🔄 **Assist recycling companies** in waste processing.
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- ⚡ **Support biogas plants** in energy production.
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"""
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)
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# User role selection
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user_role = st.selectbox("🔹 Select Your Role:", ["Citizen", "Municipal Worker", "Recycling Company", "Biogas Plant"])
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# Chat input
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user_prompt = st.chat_input(f"💬 [{user_role}] Enter your query or task...")
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if user_prompt:
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# Display the user's message
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st.chat_message("user").markdown(f"**{user_role}:** {user_prompt}")
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# Generate a role-specific prompt
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role_specific_prompt = f"You are assisting a {user_role} in a smart waste management system. The user says: {user_prompt}"
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# Send the prompt to Gemini-Pro and get the response
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try:
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gemini_response = st.session_state.chat_session.send_message(role_specific_prompt)
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# Display Gemini-Pro's response
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with st.chat_message("assistant"):
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st.markdown(gemini_response.text)
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except Exception as e:
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st.error(f"❌ Error processing your message: {e}")
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# Sidebar Information
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st.sidebar.title("📌 About")
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st.sidebar.markdown(
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"""
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- ⚡ Support biogas plants in energy production.
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"""
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import streamlit as st
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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# Streamlit page config
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st.set_page_config(page_title="BigMart Sales Predictor", page_icon="🛒", layout="centered")
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st.title("🛒 BigMart Sales Prediction")
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st.markdown("Enter item details below to predict sales:")
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# Input fields
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project_name = st.text_input("📦 Project Name")
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item_weight = st.number_input("⚖️ Item Weight (kg)", min_value=0.0, step=0.1)
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item_visibility = st.slider("👀 Item Visibility", 0.0, 1.0, 0.1)
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item_mrp = st.number_input("💰 Item MRP (Max Retail Price)", min_value=0.0, step=1.0)
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# Predict button
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if st.button("Predict Sales"):
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if not project_name:
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st.warning("Please enter a project name.")
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else:
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# Dummy ML Model: Replace with your actual trained model
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X_train = np.array([
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[9.3, 0.016, 249.8],
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[5.92, 0.019, 48.27],
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[17.5, 0.016, 141.62],
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[19.2, 0.0075, 182.095],
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])
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y_train = np.array([3735.14, 443.42, 2233.6, 3612.47]) # example sales
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model = LinearRegression()
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model.fit(X_train, y_train)
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# Prepare input
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user_input = np.array([[item_weight, item_visibility, item_mrp]])
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prediction = model.predict(user_input)[0]
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st.success(f"📈 Predicted Sales for '{project_name}': ₹{prediction:,.2f}")
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# Sidebar info
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st.sidebar.title("📌 About")
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st.sidebar.markdown(
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"""
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This app uses a simple ML model to estimate sales based on:
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- Item weight
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- Item visibility
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- Item MRP
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Replace with a trained BigMart dataset model for production use.
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"""
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