Update app.py

app.py

@@ -1,138 +0,0 @@
-# --------------------------------------------------------------
-# 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)