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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import torch
+import openai
+import os
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import matplotlib.pyplot as plt
+import io
+
+# Set up Groq API
+openai.api_key = os.getenv("GROQ_API_KEY")
+openai.api_base = "https://api.groq.com/openai/v1"
+GROQ_MODEL = "llama3-8b-8192"
+
+# Load Excel file
+@st.cache_data
+def load_excel(file):
+    xl = pd.read_excel(file, sheet_name=None)
+    all_data = pd.concat(xl.values(), ignore_index=True)
+    return all_data
+
+# Chunk data
+def chunk_data(df, chunk_size=5):
+    chunks = []
+    for i in range(0, len(df), chunk_size):
+        chunk = df.iloc[i:i+chunk_size].to_string(index=False)
+        chunks.append(chunk)
+    return chunks
+
+# Embed chunks
+@st.cache_resource
+def embed_chunks(chunks):
+    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+    embeddings = model.encode(chunks)
+    return embeddings, model
+
+# Query embedding
+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]
+
+# Generate estimate
+def generate_estimate(context, user_input):
+    prompt = f"""You are a construction estimator working in Pakistan. Using the following schedule of rates:
+
+{context}
+
+Generate a detailed BOQ estimate including item numbers, full descriptions, unit rates, quantities and total amount in Rs for:
+{user_input}
+
+Present the result in a markdown table with columns: Item No, Description, Qty, Unit, Rate, Amount."""
+    response = openai.ChatCompletion.create(
+        model=GROQ_MODEL,
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response['choices'][0]['message']['content']
+
+# Calculate quantities
+def calculate_quantities(rooms, area, baths, car_porch, living):
+    return {
+        "Total Area (sqft)": area,
+        "No. of Rooms": rooms,
+        "No. of Bathrooms": baths,
+        "Living Rooms": living,
+        "Car Porch Area (est.)": car_porch * 200
+    }
+
+# Generate scaled sketch
+def draw_floor_plan(rooms, baths, living, car_porch, area):
+    total_spaces = rooms + baths + living + car_porch
+    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(area) / 10  # Simple scale factor
+    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=8)
+
+    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
+
+# Main app
+def main():
+    st.set_page_config(page_title="Construction Estimator", layout="centered")
+    st.title("🧱 Construction Estimator (RAG + LLaMA 3 + Sketch)")
+
+    excel_file = st.file_uploader("Upload Schedule of Rates (.xlsx or .xlsm)", type=["xlsx", "xlsm"])
+    if excel_file:
+        df = load_excel(excel_file)
+        st.success("Excel file loaded successfully.")
+        chunks = chunk_data(df)
+        embeddings, model = embed_chunks(chunks)
+
+        st.subheader("🏗️ Enter Project Details")
+        rooms = st.number_input("Number of Rooms", min_value=1, value=3)
+        area = st.number_input("Total Covered Area (sqft)", min_value=100, value=1200)
+        baths = st.number_input("Number of Washrooms", 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)
+
+        if st.button("Generate Estimate"):
+            quantities = calculate_quantities(rooms, area, baths, car_porch, living)
+            user_query = f"Estimate cost for {rooms} rooms, {baths} bathrooms, {living} living rooms, total area {area} sqft, and {car_porch} car porch(es)."
+            context = query_embedding(user_query, chunks, embeddings, model)
+            response = generate_estimate(context, user_query)
+
+            st.subheader("📊 Input Quantities")
+            st.json(quantities)
+
+            st.subheader("💸 Estimated Construction Cost (BOQ Style)")
+            st.markdown(response)
+
+            # Generate Sketch
+            buf = draw_floor_plan(rooms, baths, living, car_porch, area)
+            st.subheader("🏠 Tentative Floor Plan Sketch")
+            st.image(buf, caption="Auto-generated Line Plan", use_column_width=True)
+            st.download_button("📥 Download Sketch", buf, file_name="floor_plan.png", mime="image/png")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+
+streamlit
+pandas
+numpy
+scikit-learn
+sentence-transformers
+openai==0.28.1
+matplotlib
+seaborn
+Pillow
+xlsxwriter
+openpyxl