Update app.py

app.py

@@ -1,120 +1,52 @@
-import os
 import streamlit as st
-from dotenv import load_dotenv
-import google.generativeai as gen_ai
-
-# Load environment variables
-load_dotenv()
-
-# Configure Streamlit page settings
-st.set_page_config(
-    page_title="Smart Waste Management System",
-    page_icon="♻️",
-    layout="centered",
-)
-
-# Retrieve the Google API key from the environment
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-# Check if the API key is loaded
-if not GOOGLE_API_KEY:
-    st.error("🚨 API key not found! Please set the GOOGLE_API_KEY in your .env file.")
-    st.stop()
-
-# Configure the Generative AI model
-try:
-    gen_ai.configure(api_key=GOOGLE_API_KEY)
-    model = gen_ai.GenerativeModel("gemini-1.5-pro")  # Updated model version
-except Exception as e:
-    st.error(f"❌ Error initializing the Gemini-Pro model: {e}")
-    st.stop()
-
-# Initialize the chat session if not already present in session state
-if "chat_session" not in st.session_state:
-    try:
-        st.session_state.chat_session = model.start_chat(history=[])
-    except Exception as e:
-        st.error(f"❌ Error initializing chat session: {e}")
-        st.stop()
-
-# Display the app's title
-st.title("♻️ Smart Waste Management System")
-
-# Back Button to Redirect to Dashboard
-st.markdown(
-    """
-    <style>
-        .back-button {
-            display: flex;
-            justify-content: center;
-            padding: 10px;
-        }
-        .btn {
-            background-color: #4CAF50;
-            color: white;
-            padding: 10px 20px;
-            text-align: center;
-            text-decoration: none;
-            font-size: 16px;
-            border-radius: 5px;
-            display: inline-block;
-        }
-    </style>
-    <div class="back-button">
-        <a href="https://binsight.onrender.com/dashboard.html" class="btn">🔙 Back to Dashboard</a>
-    </div>
-    """,
-    unsafe_allow_html=True,
-)
-
-# Introduction and instructions
-st.markdown(
-    """
-    Welcome to the **Smart Waste Management System**! This tool helps **citizens, municipal workers, 
-    recycling companies, and biogas plants** collaborate efficiently for **better waste management**.
-
-    ### **🌟 Key Features**
-    - 🏡 **Improve waste collection efficiency** for citizens.
-    - 🚛 **Help municipal workers** manage schedules.
-    - 🔄 **Assist recycling companies** in waste processing.
-    - ⚡ **Support biogas plants** in energy production.
-    """
-)
-
-# User role selection
-user_role = st.selectbox("🔹 Select Your Role:", ["Citizen", "Municipal Worker", "Recycling Company", "Biogas Plant"])
-
-# Chat input
-user_prompt = st.chat_input(f"💬 [{user_role}] Enter your query or task...")
-
-if user_prompt:
-    # Display the user's message
-    st.chat_message("user").markdown(f"**{user_role}:** {user_prompt}")
-
-    # Generate a role-specific prompt
-    role_specific_prompt = f"You are assisting a {user_role} in a smart waste management system. The user says: {user_prompt}"
-
-    # Send the prompt to Gemini-Pro and get the response
-    try:
-        gemini_response = st.session_state.chat_session.send_message(role_specific_prompt)
-
-        # Display Gemini-Pro's response
-        with st.chat_message("assistant"):
-            st.markdown(gemini_response.text)
-
-    except Exception as e:
-        st.error(f"❌ Error processing your message: {e}")
-
-# Sidebar Information
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+
+# Streamlit page config
+st.set_page_config(page_title="BigMart Sales Predictor", page_icon="🛒", layout="centered")
+
+st.title("🛒 BigMart Sales Prediction")
+st.markdown("Enter item details below to predict sales:")
+
+# Input fields
+project_name = st.text_input("📦 Project Name")
+item_weight = st.number_input("⚖️ Item Weight (kg)", min_value=0.0, step=0.1)
+item_visibility = st.slider("👀 Item Visibility", 0.0, 1.0, 0.1)
+item_mrp = st.number_input("💰 Item MRP (Max Retail Price)", min_value=0.0, step=1.0)
+
+# Predict button
+if st.button("Predict Sales"):
+    if not project_name:
+        st.warning("Please enter a project name.")
+    else:
+        # Dummy ML Model: Replace with your actual trained model
+        X_train = np.array([
+            [9.3, 0.016, 249.8],
+            [5.92, 0.019, 48.27],
+            [17.5, 0.016, 141.62],
+            [19.2, 0.0075, 182.095],
+        ])
+        y_train = np.array([3735.14, 443.42, 2233.6, 3612.47])  # example sales
+
+        model = LinearRegression()
+        model.fit(X_train, y_train)
+
+        # Prepare input
+        user_input = np.array([[item_weight, item_visibility, item_mrp]])
+        prediction = model.predict(user_input)[0]
+
+        st.success(f"📈 Predicted Sales for '{project_name}': ₹{prediction:,.2f}")
+
+# Sidebar info
 st.sidebar.title("📌 About")
 st.sidebar.markdown(
     """
-    **The Smart Waste Management System aims to:**
-    - 🏡 Improve waste collection efficiency for citizens.
-    - 🚛 Help municipal workers manage schedules.
-    - 🔄 Assist recycling companies in waste processing.
-    - ⚡ Support biogas plants in energy production.
+    This app uses a simple ML model to estimate sales based on:
+    - Item weight
+    - Item visibility
+    - Item MRP
 
-    💡 **Need Help?** Use the chat to ask questions!
+    Replace with a trained BigMart dataset model for production use.
     """
 )