Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import ezdxf
 import io
 import pandas as pd
+import matplotlib.pyplot as plt
 from fpdf import FPDF
 from tempfile import NamedTemporaryFile
 
@@ -16,21 +17,20 @@ DEFAULT_WALL_THICKNESS = 0.75
 st.set_page_config(page_title="Building Estimator from CAD", layout="wide")
 st.title("🏗️ Auto Estimation from AutoCAD (.dxf) Drawing")
 
-# Upload DXF file
 uploaded_file = st.file_uploader("Upload your DXF file", type=["dxf"])
 
 @st.cache_data
 def extract_geometry(file_bytes):
-    # Wrap file in text buffer and read as ASCII
     try:
         text_buffer = io.TextIOWrapper(file_bytes, encoding='utf-8', errors='ignore')
         doc = ezdxf.read(text_buffer)
     except Exception as e:
         st.error(f"Error reading DXF file: {e}")
-        return [], 0.75, [], 1
+        return [], 0.75, [], 1, []
 
     msp = doc.modelspace()
     rooms = []
+    room_shapes = []
     openings = []
     floors = 1
     wall_thickness = DEFAULT_WALL_THICKNESS
@@ -54,25 +54,21 @@ def extract_geometry(file_bytes):
                 points = entity.get_points()
                 x_vals = [p[0] for p in points]
                 y_vals = [p[1] for p in points]
-                length = abs(max(x_vals) - min(x_vals)) / 12  # inches to ft
+                length = abs(max(x_vals) - min(x_vals)) / 12
                 width = abs(max(y_vals) - min(y_vals)) / 12
-                height = 10  # default wall height in ft
-                if length > 2 and width > 2:  # Room
+                height = 10
+                if length > 2 and width > 2:
                     rooms.append((length, width, height))
+                    room_shapes.append((min(x_vals), min(y_vals), max(x_vals), max(y_vals)))
                 else:
                     openings.append(("opening", length, height))
-    return rooms, wall_thickness, openings, floors
 
-def estimate(rooms, wall_thickness, openings, floors):
-    wall_volume = 0
-    for length, width, height in rooms:
-        wall_area = 2 * (length + width) * height
-        wall_volume += wall_area * wall_thickness
+    return rooms, wall_thickness, openings, floors, room_shapes
 
+def estimate(rooms, wall_thickness, openings, floors):
+    wall_volume = sum(2 * (l + w) * h * wall_thickness for l, w, h in rooms)
     opening_volume = sum(l * h * wall_thickness for _, l, h in openings)
-
     beam_volume = sum((l + 1) * 0.75 * 0.75 for _, l, _ in openings)
-
     net_volume = (wall_volume - opening_volume - beam_volume) * floors
 
     number_of_bricks = round((net_volume / BRICK_VOLUME_CFT) * 1.05)
@@ -90,43 +86,51 @@ def generate_pdf(data_dict):
     pdf.set_font("Arial", size=12)
     pdf.cell(200, 10, "Estimation Report", ln=True, align='C')
     pdf.ln(10)
-
     for key, value in data_dict.items():
         pdf.cell(200, 10, f"{key}: {value}", ln=True)
-
     tmp = NamedTemporaryFile(delete=False, suffix=".pdf")
     pdf.output(tmp.name)
     return tmp.name
 
+def plot_rooms(shapes):
+    fig, ax = plt.subplots()
+    for x0, y0, x1, y1 in shapes:
+        width = (x1 - x0)
+        height = (y1 - y0)
+        rect = plt.Rectangle((x0, y0), width, height, fill=False, edgecolor='blue', linewidth=2)
+        ax.add_patch(rect)
+    ax.set_title("🧭 2D Floor Plan")
+    ax.set_aspect("equal")
+    ax.axis("off")
+    st.pyplot(fig)
+
 if uploaded_file:
     file_bytes = io.BytesIO(uploaded_file.read())
-    rooms, wall_thickness, openings, floors = extract_geometry(file_bytes)
+    rooms, wall_thickness, openings, floors, room_shapes = extract_geometry(file_bytes)
 
     st.success(f"✔️ Parsed {len(rooms)} rooms and {len(openings)} openings across {floors} floor(s).")
     st.write(f"📏 Wall Thickness: {wall_thickness} ft")
 
     bricks, sand, cement = estimate(rooms, wall_thickness, openings, floors)
 
-    st.subheader("📊 Estimation Result")
-    st.write(f"🧱 **Bricks Required:** {bricks:,}")
-    st.write(f"🏖️ **Sand Volume:** {sand:.2f} cft")
-    st.write(f"🪣 **Cement Bags:** {cement} (50kg each)")
+    st.subheader("📐 Room Dimensions")
+    df_rooms = pd.DataFrame(rooms, columns=["Length (ft)", "Width (ft)", "Height (ft)"])
+    st.dataframe(df_rooms)
 
-    # Table
-    df = pd.DataFrame({
+    st.subheader("🧱 Estimation Result")
+    df_result = pd.DataFrame({
         "Item": ["Bricks", "Sand (cft)", "Cement (bags)"],
         "Quantity": [bricks, round(sand, 2), cement]
     })
+    st.dataframe(df_result)
 
-    st.dataframe(df)
+    st.subheader("🖼️ 2D Floor Plan")
+    plot_rooms(room_shapes)
 
-    # Export
     st.subheader("📤 Export")
     col1, col2 = st.columns(2)
-
     with col1:
-        st.download_button("📥 Download as Excel", df.to_csv(index=False).encode(), "estimates.csv", "text/csv")
-
+        st.download_button("⬇️ Download Excel", df_result.to_csv(index=False).encode(), "estimates.csv", "text/csv")
     with col2:
         pdf_file = generate_pdf({
             "Bricks Required": f"{bricks:,}",
@@ -134,6 +138,7 @@ if uploaded_file:
             "Cement Bags": f"{cement} bags"
         })
         with open(pdf_file, "rb") as f:
-            st.download_button("📄 Download as PDF", f.read(), "estimates.pdf", "application/pdf")
+            st.download_button("⬇️ Download PDF", f.read(), "estimates.pdf", "application/pdf")
+