Create app.py

app.py

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+# --------------------------------------------------------------
+# combined_s3_sql_vector_app.py
+# Full Combined: S3 SQL + Vector MCP Tool in One Gradio App
+# --------------------------------------------------------------
+import boto3
+import pandas as pd
+import duckdb
+import requests
+from urllib.parse import quote
+from io import StringIO
+import gradio as gr
+
+# === S3 Credentials ===
+ENDPOINT_URL = "https://s3.us-west-1.idrivee2.com"
+ACCESS_KEY = "rNuPBAQetemqpEeBospZ"
+SECRET_KEY = "BU4FccUYxzXVqiWjPSJM1CWEX1cNhBqbU9NeGidE"
+BUCKET = "accusagas3"
+
+s3 = boto3.client(
+    "s3",
+    endpoint_url=ENDPOINT_URL,
+    aws_access_key_id=ACCESS_KEY,
+    aws_secret_access_key=SECRET_KEY,
+)
+
+# --------------------------------------------------------------
+# Vector MCP Tool
+# --------------------------------------------------------------
+def query_vector_agent_calling(user_query: str, collection_name: str) -> str:
+    base_url = "https://srivatsavdamaraju-mvp-2-0-deploy-all-apis.hf.space/qdrant/search"
+    encoded_collection = quote(collection_name, safe="")
+
+    url = f"{base_url}?collection_name={encoded_collection}&mode=hybrid"
+
+    headers = {
+        "accept": "application/json",
+        "Content-Type": "application/json",
+    }
+
+    payload = {"query": user_query, "top_k": 5}
+
+    try:
+        response = requests.post(url, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+
+        data = response.json()
+        results = data.get("results") or data.get("result") or []
+
+        if not results:
+            return "No relevant context found."
+
+        output = []
+        for item in results:
+            text = item.get("text") or item.get("payload", {}).get("text") or str(item)
+            score = item.get("score", "?")
+            output.append(f"Score: {score}\n{text}\n---")
+
+        return "\n".join(output)
+
+    except requests.exceptions.Timeout:
+        return "Vector API timeout."
+    except requests.exceptions.HTTPError as e:
+        return f"HTTP Error: {e.response.status_code}"
+    except Exception as e:
+        return f"Unexpected Error: {str(e)}"
+
+
+# --------------------------------------------------------------
+# SQL Query Tool (S3 → DuckDB)
+# --------------------------------------------------------------
+def run_sql(path: str, sql: str) -> pd.DataFrame:
+    try:
+        obj = s3.get_object(Bucket=BUCKET, Key=path)
+        df = pd.read_csv(StringIO(obj["Body"].read().decode("utf-8")))
+    except Exception as e:
+        return pd.DataFrame({"error": [str(e)]})
+
+    if df.empty:
+        return pd.DataFrame({"error": ["Empty CSV"]})
+
+    for col in df.columns:
+        if any(x in col.lower() for x in ["price", "volume", "amount"]):
+            df[col] = pd.to_numeric(df[col].astype(str).str.replace(r"[^\d.-]", "", regex=True), errors="coerce")
+
+    con = duckdb.connect(":memory:")
+    con.register("data", df)
+
+    if not sql.strip().lower().startswith(("select", "with")):
+        con.close()
+        return pd.DataFrame({"error": ["Only SELECT allowed"]})
+
+    try:
+        result = con.execute(sql).df()
+    except Exception as e:
+        if "VARCHAR" in str(e):
+            import re
+            col = re.search(r"column ([a-zA-Z0-9_]+)", str(e))
+            if col and (c := col.group(1)) in df.columns:
+                sql = sql.replace(c, f"CAST({c} AS DOUBLE)")
+                result = con.execute(sql).df()
+            else:
+                con.close()
+                return pd.DataFrame({"error": [str(e)]})
+        else:
+            con.close()
+            return pd.DataFrame({"error": [str(e)]})
+    finally:
+        con.close()
+
+    return result.head(10000)
+
+
+# --------------------------------------------------------------
+# Combined Gradio App (with MCP enabled)
+# --------------------------------------------------------------
+with gr.Blocks() as app:
+    gr.Markdown("# 🔥 Combined S3 SQL + Vector MCP Tool")
+
+    gr.Markdown("## 📌 SQL Query on S3 CSV Files")
+    with gr.Row():
+        path = gr.Textbox(label="S3 Path", placeholder="folder/file.csv")
+        sql = gr.Textbox(label="SQL Query", lines=3, placeholder="SELECT * FROM data LIMIT 10")
+    btn_sql = gr.Button("Run SQL Query")
+    out_sql = gr.Dataframe()
+
+    gr.Markdown("---\n## 🔍 Vector Search MCP Tool")
+    with gr.Row():
+        user_query = gr.Textbox(label="Query", placeholder="Explain gold market trends")
+        collection_name = gr.Textbox(label="Collection Name", placeholder="gold&silver-db")
+    btn_vec = gr.Button("Run Vector Search")
+    out_vec = gr.Textbox(label="Vector Output", lines=10)
+
+    btn_sql.click(run_sql, [path, sql], out_sql)
+    btn_vec.click(query_vector_agent_calling, [user_query, collection_name], out_vec)
+
+
+if __name__ == "__main__":
+    app.launch(server_name="0.0.0.0", server_port=7860, mcp_server=True)