Create app.py

app.py

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+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 {HUGGINGFACE_API_TOKEN}",
+    "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")