Update app.py

app.py

@@ -1,16 +1,32 @@
 # --------------------------------------------------------------
-# vector_mcp_only.py — Standalone Vector MCP Tool Gradio App
+# combined_s3_sql_vector_app.py
+# Full Combined: S3 SQL + Vector MCP Tool in One Gradio App
 # --------------------------------------------------------------
-import gradio as gr
+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:
-    """
-    Standalone Vector Search MCP tool.
-    Calls HF API with URL-encoded collection name.
-    """
     base_url = "https://srivatsavdamaraju-mvp-2-0-deploy-all-apis.hf.space/qdrant/search"
     encoded_collection = quote(collection_name, safe="")
 
@@ -31,42 +47,92 @@ def query_vector_agent_calling(user_query: str, collection_name: str) -> str:
         results = data.get("results") or data.get("result") or []
 
         if not results:
-            return "⚠️ No relevant results found."
+            return "No relevant context found."
 
         output = []
-        for r in results:
-            text = (
-                r.get("text")
-                or r.get("payload", {}).get("text")
-                or str(r)
-            )
-            if text:
-                score = r.get("score", "?")
-                output.append(f"Score: {score}\n{text}\n---")
+        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 "⏳ Request timed out. Try again."
+        return "Vector API timeout."
     except requests.exceptions.HTTPError as e:
-        return f"HTTP Error: {e.response.status_code}\n{e.response.text}"
+        return f"HTTP Error: {e.response.status_code}"
     except Exception as e:
         return f"Unexpected Error: {str(e)}"
 
 
-# ------------------ Gradio UI ------------------
+# --------------------------------------------------------------
+# 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("## 🔍 Vector Search MCP Tool (Standalone)")
+    gr.Markdown("# 🔥 Combined S3 SQL + Vector MCP Tool")
 
+    gr.Markdown("## 📌 SQL Query on S3 CSV Files")
     with gr.Row():
-        user_query = gr.Textbox(label="Your Query", placeholder="e.g., gold market trend", lines=2)
-        collection_name = gr.Textbox(label="Collection Name", placeholder="e.g., gold&silver-reports")
+        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()
 
-    btn = gr.Button("Run Vector Search")
-    out = gr.Textbox(label="Search Result", lines=10)
+    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.click(query_vector_agent_calling, [user_query, collection_name], out)
+    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=7861, mcp_server=True)
+    app.launch(server_name="0.0.0.0", server_port=7860, mcp_server=True)