Update app.py

app.py

@@ -1,124 +1,107 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import matplotlib.pyplot as plt
 import io
+import os
+import replicate
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+from openai import OpenAI
 
 st.set_page_config(page_title="Construction Estimator", layout="centered")
 
-def calculate_scaled_quantities(base_qty_df, covered_area, floors):
-    """
-    base_qty_df: DataFrame with columns: Material, Unit, Quantity_per_1000sqft
-    covered_area: float, in sqft
-    floors: int, total floors (including ground floor)
-
-    Returns a DataFrame with scaled quantities according to covered area and floors
-    """
-    scale_factor = covered_area / 1000
-    ground_floor_qty = base_qty_df['Quantity_per_1000sqft'] * scale_factor
-    # Additional floors at 80% of ground floor quantities
-    additional_floors_qty = ground_floor_qty * 0.8 * (floors - 1) if floors > 1 else 0
-
-    base_qty_df['Scaled Quantity'] = ground_floor_qty + additional_floors_qty
-    return base_qty_df
-
-def calculate_costs(qty_df, rates_df):
-    df = pd.merge(qty_df, rates_df, on=['Material', 'Unit'], how='left')
-    df['Amount'] = df['Scaled Quantity'] * df['Rate_per_Unit']
-    df.fillna({'Rate_per_Unit':0, 'Amount':0}, inplace=True)
-    return df
-
-def generate_boq_table(df):
-    df = df.reset_index(drop=True)
-    df.index += 1
-    df_out = df.rename(columns={
-        'Material': 'Description',
-        'Scaled Quantity': 'Qty',
-        'Unit': 'Unit',
-        'Rate_per_Unit': 'Rate (PKR)',
-        'Amount': 'Amount (PKR)'
-    })[['Description', 'Qty', 'Unit', 'Rate (PKR)', 'Amount (PKR)']]
-    return df_out
-
-def draw_floor_plan(rooms, baths, living, car_porch, covered_area):
-    total_spaces = rooms + baths + living + car_porch
-    if total_spaces == 0:
-        total_spaces = 1
-    cols = int(np.ceil(np.sqrt(total_spaces)))
-    rows = int(np.ceil(total_spaces / cols))
-
-    fig, ax = plt.subplots(figsize=(10, 8))
-    scale = np.sqrt(covered_area) / 10
-    width, height = scale, scale * 0.75
-
-    labels = (["Room"] * rooms + ["Bath"] * baths + ["Living"] * living + ["Car Porch"] * car_porch)
-    for i, label in enumerate(labels):
-        row = i // cols
-        col = i % cols
-        x = col * width
-        y = (rows - 1 - row) * height
-        ax.add_patch(plt.Rectangle((x, y), width, height, edgecolor='black', facecolor='lightblue'))
-        ax.text(x + width/2, y + height/2, label, ha='center', va='center', fontsize=10)
-
-    ax.set_xlim(0, cols*width)
-    ax.set_ylim(0, rows*height)
-    ax.set_aspect('equal')
-    ax.set_title(f"Tentative Floor Plan (Scale: 1 unit = {int(scale)} sqft)")
-    ax.axis('off')
-
-    buf = io.BytesIO()
-    plt.savefig(buf, format='png')
-    buf.seek(0)
-    return buf
-
-def main():
-    st.title("🧱 Construction Material Estimator with Floors")
-
-    st.markdown("Upload **Base Quantities per 1000 sqft** and **Material Rates** Excel files. Then enter project details including number of floors.")
-
-    qty_file = st.file_uploader("Upload Base Quantities Excel (Material, Unit, Quantity_per_1000sqft)", type=["xlsx", "xls"])
-    rate_file = st.file_uploader("Upload Material Rates Excel (Material, Unit, Rate_per_Unit)", type=["xlsx", "xls"])
-
-    if qty_file and rate_file:
-        try:
-            qty_df = pd.read_excel(qty_file)
-            rate_df = pd.read_excel(rate_file)
-
-            required_qty_cols = {'Material', 'Unit', 'Quantity_per_1000sqft'}
-            required_rate_cols = {'Material', 'Unit', 'Rate_per_Unit'}
-
-            if not required_qty_cols.issubset(set(qty_df.columns)):
-                st.error(f"Base Quantities Excel missing columns: {required_qty_cols - set(qty_df.columns)}")
-                return
-            if not required_rate_cols.issubset(set(rate_df.columns)):
-                st.error(f"Material Rates Excel missing columns: {required_rate_cols - set(rate_df.columns)}")
-                return
-
-            covered_area = st.number_input("Covered Area (sqft)", min_value=100, value=1200)
-            floors = st.number_input("Number of Floors", min_value=1, value=1)
-            rooms = st.number_input("Number of Rooms", min_value=0, value=3)
-            baths = st.number_input("Number of Bathrooms", min_value=0, value=2)
-            living = st.number_input("Number of Living Rooms", min_value=0, value=1)
-            car_porch = st.number_input("Number of Car Porches", min_value=0, value=1)
-
-            if st.button("Calculate BOQ & Cost"):
-                scaled_qty_df = calculate_scaled_quantities(qty_df, covered_area, floors)
-                cost_df = calculate_costs(scaled_qty_df, rate_df)
-                boq_table = generate_boq_table(cost_df)
-
-                st.subheader("Scaled Material Quantities & Cost Estimate")
-                st.dataframe(boq_table.style.format({"Qty": "{:.2f}", "Rate (PKR)": "₨ {:,.2f}", "Amount (PKR)": "₨ {:,.2f}"}))
-
-                total_cost = boq_table['Amount (PKR)'].sum()
-                st.markdown(f"### Total Estimated Cost: ₨ {total_cost:,.2f}")
-
-                buf = draw_floor_plan(rooms, baths, living, car_porch, covered_area)
-                st.subheader("Tentative Floor Plan Sketch")
-                st.image(buf, use_column_width=True)
-                st.download_button("Download Floor Plan Sketch", buf, file_name="floor_plan.png", mime="image/png")
-
-        except Exception as e:
-            st.error(f"Error processing files: {e}")
-
-if __name__ == "__main__":
-    main()
+client = OpenAI(
+    api_key=os.getenv("GROQ_API_KEY"),
+    base_url="https://api.groq.com/openai/v1"
+)
+GROQ_MODEL = "llama3-8b-8192"
+
+@st.cache_data
+def load_excel(file):
+    return pd.read_excel(file)
+
+@st.cache_resource
+def embed_chunks(chunks):
+    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+    embeddings = model.encode(chunks)
+    return embeddings, model
+
+def query_embedding(user_query, chunks, embeddings, model):
+    query_vec = model.encode([user_query])
+    similarities = cosine_similarity(query_vec, embeddings)[0]
+    top_idx = np.argmax(similarities)
+    return chunks[top_idx]
+
+def generate_estimate(context, user_input):
+    prompt = f"""You are a construction estimator in Pakistan. Using the following schedule:
+
+{context}
+
+Generate a BOQ with item number, description, quantity, unit, rate, and total amount for:
+{user_input}
+
+Output as a markdown table."""
+
+    response = client.chat.completions.create(
+        model=GROQ_MODEL,
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response.choices[0].message.content
+
+def compute_total_quantities(base_df, covered_area, floors):
+    base_df = base_df.copy()
+    base_df["Adjusted Qty"] = base_df["Qty per 1000 sft"] * (covered_area / 1000)
+    floor_factor = 1 + max(0, floors - 1) * 0.8
+    base_df["Total Qty"] = base_df["Adjusted Qty"] * floor_factor
+    return base_df
+
+def generate_realistic_plan(rooms, baths, living, car_porch):
+    prompt = f"floor plan for {rooms} rooms, {baths} bathrooms, {living} living rooms, and {car_porch} car porch in modern style"
+    output = replicate.run(
+        "cjwbw/floor-plan-generator",
+        input={"prompt": prompt}
+    )
+    return output
+
+st.title("🏗️ Construction Estimator (Material + Cost + BOQ + Sketch)")
+
+quantity_file = st.file_uploader("Upload Material Quantities Excel (per 1000 sft)", type=["xlsx"])
+cost_file = st.file_uploader("Upload Material Costs Excel", type=["xlsx"])
+
+if quantity_file and cost_file:
+    base_df = load_excel(quantity_file)
+    cost_df = load_excel(cost_file)
+
+    if "Material" in base_df.columns and "Qty per 1000 sft" in base_df.columns:
+        st.success("Files loaded successfully.")
+
+        rooms = st.number_input("Number of Rooms", min_value=1, value=3)
+        baths = st.number_input("Number of Bathrooms", min_value=1, value=2)
+        living = st.number_input("Number of Living Rooms", min_value=0, value=1)
+        car_porch = st.number_input("Number of Car Porches", min_value=0, value=1)
+        covered_area = st.number_input("Total Covered Area (sft)", min_value=100, value=1200)
+        floors = st.number_input("Number of Floors", min_value=1, value=1)
+
+        if st.button("Generate Estimate"):
+            computed_df = compute_total_quantities(base_df, covered_area, floors)
+            boq = computed_df.merge(cost_df, on="Material", how="left")
+            boq["Amount"] = boq["Total Qty"] * boq["Rate"]
+
+            st.subheader("📋 Bill of Quantities (BOQ)")
+            st.dataframe(boq[["Material", "Total Qty", "Unit", "Rate", "Amount"]])
+
+            total_cost = boq["Amount"].sum()
+            st.metric("Total Estimated Cost (Rs)", f"{total_cost:,.0f}")
+
+            st.subheader("🏠 AI-Generated Floor Plan Sketch")
+            try:
+                image_url = generate_realistic_plan(rooms, baths, living, car_porch)
+                st.image(image_url, caption="Generated by AI (Replicate)", use_column_width=True)
+            except Exception as e:
+                st.warning("Could not generate sketch. Please check Replicate API setup.")
+                st.text(str(e))
+
+    else:
+        st.error("Quantity sheet must contain 'Material' and 'Qty per 1000 sft' columns.")
+else:
+    st.info("Please upload both the quantity and cost Excel sheets.")