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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+real_estate.db filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,104 @@
+from smolagents import tool, CodeAgent, InferenceClientModel
+import os
+import dotenv
+from sqlalchemy import create_engine, text
+import gradio as gr
+
+dotenv.load_dotenv()
+
+DB_PATH = "real_estate.db"
+MODEL_ID = "openai/gpt-oss-120b" 
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+if HF_TOKEN is None:
+    raise RuntimeError("HF_TOKEN missing. Add it in Space Settings → Repository secrets.")
+
+engine = create_engine(f"sqlite:///{DB_PATH}", echo=True, future=True)
+
+@tool
+def sql_engine(query: str) -> str:
+    """
+    Executes a SQL query on the 'real_estate.db' SQLite database and returns the results as a string. The database contains a table named 'real_estate' with the following schema:
+        Columns:
+    - id: TEXT — Unique identifier of the listing.
+    - ref: TEXT — Reference number of the property listing.
+    - title: TEXT — Title or headline of the property listing.
+    - no_of_leads: BIGINT — Number of customer leads or inquiries for the property.
+    - location: TEXT — Full address or general location of the property.
+    - sublocation: TEXT — Sub-location or neighborhood within the main location.
+    - building: TEXT — Name of the building in which the property is located.
+    - unit: TEXT — Unit number or identifier within the building.
+    - price: FLOAT — Price of the property in AED (United Arab Emirates Dirham).
+    - category: TEXT — Category or type of property (e.g., Apartment, Villa, Townhouse, Duplex, Penthouse, Hotel Apartment, Land Residential, Loft Apartment, Residential Building, Bungalow, Full Floor Residential, Bulk Units).
+    - score: BIGINT — Quality or ranking score of the property (0–100 scale).
+    - beds: FLOAT — Number of bedrooms in the property.
+    - baths: FLOAT — Number of bathrooms in the property.
+    - property_for: TEXT — Type of contract: either "Rental" or "Sales".
+    - views: TEXT — List of view features (comma-separated, e.g., "Sea View, City View").
+    - features: TEXT — Comma-separated list of property features (e.g., "Balcony, Pool, Parking").
+    - furnishing: TEXT — Furnishing status (e.g., Partly Furnished, Unfurnished, Furnished, Fitted, Not Fitted, Shell And Core).
+    - assigned_agent_name: TEXT — Name of the agent currently assigned to the property.
+    - marketed_agent_name: TEXT — Name of the agent marketing the property.
+    - portals: TEXT — List of online portals where the property is listed (comma-separated).
+
+    Use LIKE for partial matches on fields like location, sublocation, building, features, and portals, assigned_agent_name, marketed_agent_name, views, features.
+    Args:
+        query (str): The SQL query to be executed.
+
+    """
+    output = ""
+
+    with engine.connect() as connection:
+        result = connection.execute(text(query))
+        rows = result.fetchall()
+        if not rows:
+            return "No results found."
+        for row in rows:
+            output += str(row) + "\n"
+    return output.strip()
+
+def make_agent():
+
+    agent = CodeAgent(model= InferenceClientModel(model_id=MODEL_ID, api_key=HF_TOKEN),
+                    tools=[sql_engine],
+                    description="A helpful agent that can answer questions about real estate properties in Dubai based on a database of listings.",
+                    )
+
+    return agent
+
+agent = make_agent()
+
+def run_agent(user_request: str) -> str:
+    """
+    Takes a natural language request, lets the agent decide the SQL via the tool,
+    and returns a friendly response.
+    """
+    if not user_request or not user_request.strip():
+        return "Please enter a request."
+    try:
+        return agent.run(user_request.strip())
+    except Exception as e:
+        return f"Agent error: {e}"
+
+# ---- Gradio UI ----
+with gr.Blocks(title="Real Estate Text-to-SQL") as demo:
+    gr.Markdown(
+        """
+        # 🏡 Nazem's personal Driven Text-to-SQL Agent
+        Ask natural-language questions to the CRM's listings database.
+
+        **Examples**
+        - "Write me a message to send my client who is asking me to propose 3 properties to show him. He is looking for flat with 2 to 3 bedrooms a marina view and a swimming pool between 1 and 5 million AED."
+        - "Show 5 apartments in Dubai Marina with ≥3 beds, ≥2 baths, price 1M–2M AED. Include id, title, location, price, beds, baths, features."
+        - "Top 10 cheapest villas in Palm Jumeirah with at least 4 bedrooms."
+        - "
+        """
+    )
+    with gr.Row():
+        inp = gr.Textbox(label="Your request", lines=5, placeholder="Ask for properties, filters, columns, limits…")
+    btn = gr.Button("Run")
+    out = gr.Markdown(label="Response")
+
+    btn.click(run_agent, inputs=inp, outputs=out)
+
+if __name__ == "__main__":
+    demo.launch()

real_estate.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4c398984a2692840c6e999c0ab37fab993b6f598c7bdd7bafa3b1dc32aaa4a5
+size 3088384

requirements.txt

@@ -0,0 +1,4 @@
+smolagents>=0.3
+sqlalchemy>=2.0
+gradio>=4.0
+python-dotenv>=1.0