Spaces:
Build error
Build error
File size: 3,478 Bytes
7dbf6ae 334ec42 7dbf6ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import streamlit as st
import requests
import pandas as pd
# ---------------------- App Title ----------------------
st.title("🏗️ BuildSmart Estimator")
st.markdown("Estimate construction materials using a Mistral-powered model via Hugging Face.")
# ---------------------- User Inputs ----------------------
st.header("📋 Project Details")
area = st.number_input("Total Area (in square feet)", min_value=100, max_value=100000, step=100)
floors = st.number_input("Number of Floors", min_value=1, max_value=100, step=1)
structure_type = st.selectbox("Structure Type", ["Residential", "Commercial", "Industrial"])
material_preference = st.selectbox("Material Preference", ["Cement & Bricks", "Steel & Concrete"])
location = st.text_input("Location")
# ---------------------- Hugging Face Config ----------------------
HUGGINGFACE_API_TOKEN = st.secrets["api_token"]
HUGGINGFACE_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
headers = {
"Authorization": f"Bearer {MISTRAL_API}",
"Content-Type": "application/json"
}
# ---------------------- Build Prompt ----------------------
def build_prompt(area, floors, structure_type, material_pref, location):
return (
f"[INST] Estimate construction materials for the following project:\n"
f"- Area: {area} sqft\n"
f"- Floors: {floors}\n"
f"- Structure type: {structure_type}\n"
f"- Material preference: {material_pref}\n"
f"- Location: {location}\n\n"
f"Return in this format:\n"
f"Cement (bags), Sand (cubic feet), Bricks (units), Steel (kg), Crush (cubic feet), Rori (cubic feet). [/INST]"
)
# ---------------------- Call API ----------------------
def query_mistral(prompt):
response = requests.post(
HUGGINGFACE_API_URL,
headers=headers,
json={"inputs": prompt}
)
if response.status_code == 200:
return response.json()[0]["generated_text"]
else:
return f"❌ API Error {response.status_code}: {response.text}"
# ---------------------- Submit Button ----------------------
if st.button("Estimate Materials"):
if not location:
st.warning("Please enter a location before submitting.")
else:
with st.spinner("Estimating materials using Mistral..."):
prompt = build_prompt(area, floors, structure_type, material_preference, location)
result_text = query_mistral(prompt)
st.subheader("📦 Estimated Materials")
st.text(result_text)
try:
lines = result_text.strip().split(",")
data = []
for line in lines:
if ":" in line:
key, value = line.split(":", 1)
else:
parts = line.strip().split()
key = " ".join(parts[:-1])
value = parts[-1]
data.append([key.strip(), value.strip()])
df = pd.DataFrame(data, columns=["Material", "Estimated Quantity"])
st.dataframe(df)
csv = df.to_csv(index=False)
st.download_button("📥 Download as CSV", csv, "material_estimate.csv", "text/csv")
except Exception as e:
st.error("❗ Could not parse the model output.")
st.exception(e)
st.markdown("---")
st.caption("Powered by Mistral via Hugging Face Inference API")
|