Create app.py

app.py

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+import streamlit as st
+import requests
+import csv
+import io
+
+# -------------------------------
+# BuildSmart Estimator: Streamlit Web App
+# -------------------------------
+
+# Set page config
+st.set_page_config(page_title="BuildSmart Estimator", page_icon="🏗️")
+
+# App title
+st.title("🏗️ BuildSmart Estimator")
+st.subheader("Estimate construction materials based on project details")
+
+# Input form for user
+with st.form("estimator_form"):
+    total_area = st.number_input("Total Area (in square feet)", min_value=100, step=50)
+    floors = st.number_input("Number of Floors", min_value=1, step=1)
+    structure_type = st.selectbox("Structure Type", ["Residential", "Commercial", "Industrial"])
+    material_pref = st.selectbox("Material Preference", ["Cement & Bricks", "Steel & Concrete"])
+    location = st.text_input("Location", placeholder="e.g., Lahore, Karachi, etc.")
+    
+    # Submit button
+    submitted = st.form_submit_button("Estimate Materials")
+
+# Function to build the prompt
+def build_prompt(area, floors, structure, material, loc):
+    return (
+        f"Estimate the construction material required for a {structure} building "
+        f"with {floors} floor(s), total area of {area} square feet, located in {loc}, "
+        f"using {material}. Return quantities for: Cement (bags), Sand (cubic feet), Bricks (units), "
+        f"Steel (kg), Crush (cubic feet), and Rori (cubic feet)."
+    )
+
+# Function to call Hugging Face Inference API
+def query_huggingface_api(prompt):
+    API_URL = "https://api-inference.huggingface.co/models/your-username/your-model-name"  # 🔁 Replace this
+    headers = {
+        "Authorization": "Bearer YOUR_HUGGINGFACE_API_TOKEN"  # 🔁 Replace this
+    }
+    payload = {
+        "inputs": prompt
+    }
+    response = requests.post(API_URL, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()[0]['generated_text']
+    else:
+        return f"❌ API Error: {response.status_code} - {response.text}"
+
+# Process form submission
+if submitted:
+    prompt = build_prompt(total_area, floors, structure_type, material_pref, location)
+    with st.spinner("Estimating materials..."):
+        response_text = query_huggingface_api(prompt)
+    
+    # Display results
+    st.markdown("### 📦 Estimated Material Requirements")
+    st.text(response_text)
+
+    # Optional: Convert response into CSV format
+    csv_buffer = io.StringIO()
+    writer = csv.writer(csv_buffer)
+    writer.writerow(["Material", "Quantity"])
+
+    # Parse simple line-based format (adjust if your model returns structured JSON)
+    for line in response_text.splitlines():
+        if ":" in line:
+            parts = line.split(":", 1)
+            writer.writerow([parts[0].strip(), parts[1].strip()])
+
+    # Download button
+    st.download_button(
+        label="📥 Download Estimate",
+        data=csv_buffer.getvalue(),
+        file_name="material_estimate.csv",
+        mime="text/csv"
+    )
+
+# Footer
+st.markdown("---")
+st.caption("Developed for educational purposes. Replace placeholder API values before deployment.")