Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from rapidfuzz import process, fuzz
+
+# Load dataset once
+DF = pd.read_csv("./data/food_purine_mcp_ready_v2.csv")
+
+# ---------- MCP Tools -------------------------------------------------
+def lookup_food(name: str):
+    """
+    Return one row (food, purine, label) that exactly matches *name* (case-insensitive).
+    """
+    row = DF.loc[DF["food"].str.lower() == name.lower()]
+    if row.empty:
+        return {"error": f"No exact match for '{name}'."}
+    return row.iloc[0].to_dict()
+
+def fuzzy_search(query: str, k: int = 5, cutoff: int = 80):
+    """
+    Fuzzy-match *query* against the food column.
+    Returns up to *k* rows with WRatio ≥ *cutoff*.
+    """
+    choices = DF["food"].tolist()
+    matches = process.extract(
+        query, choices, scorer=fuzz.WRatio, score_cutoff=cutoff, limit=k
+    )
+    rows = DF.loc[DF["food"].isin([m[0] for m in matches])]
+    return rows.to_dict(orient="records")
+
+# ---------- Minimal UI (optional) -------------------------------------
+with gr.Blocks(title="Purine DB MCP") as demo:
+    gr.Markdown("## Purine Lookup Tools (MCP-enabled)")
+    with gr.Tab("Exact lookup"):
+        in1 = gr.Textbox(label="Food name")
+        out1 = gr.JSON()
+        in1.submit(lookup_food, in1, out1)
+    with gr.Tab("Fuzzy search"):
+        in2 = gr.Textbox(label="Fuzzy term")
+        out2 = gr.JSON()
+        in2.submit(fuzzy_search, in2, out2)
+
+# ---------- Launch ----------------------------------------------------
+demo.launch(
+    server_name="0.0.0.0",  # expose on container/VM
+    share=False,            # True if you want a public Gradio link
+    mcp_server=True,        # 🌟 <- THIS turns it into an MCP endpoint
+)