Create app.py

app.py

@@ -0,0 +1,72 @@
+# --------------------------------------------------------------
+# vector_mcp_only.py — Standalone Vector MCP Tool Gradio App
+# --------------------------------------------------------------
+import gradio as gr
+import requests
+from urllib.parse import quote
+
+
+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="")
+
+    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 results 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---")
+
+        return "\n".join(output)
+
+    except requests.exceptions.Timeout:
+        return "⏳ Request timed out. Try again."
+    except requests.exceptions.HTTPError as e:
+        return f"HTTP Error: {e.response.status_code}\n{e.response.text}"
+    except Exception as e:
+        return f"Unexpected Error: {str(e)}"
+
+
+# ------------------ Gradio UI ------------------
+with gr.Blocks() as app:
+    gr.Markdown("## 🔍 Vector Search MCP Tool (Standalone)")
+
+    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")
+
+    btn = gr.Button("Run Vector Search")
+    out = gr.Textbox(label="Search Result", lines=10)
+
+    btn.click(query_vector_agent_calling, [user_query, collection_name], out)
+
+
+if __name__ == "__main__":
+    app.launch(server_name="0.0.0.0", server_port=7861, mcp_server=True)