Update app.py

app.py

@@ -8,18 +8,7 @@ It exposes the following tools:
 
 import os
 from dotenv import load_dotenv
-load_dotenv()  # Load environment variables from .env file
-
-import logging
-
-# Configure logging to write to a file instead of stdout/stderr
-# This avoids interference with the MCP communication channel
-logging.basicConfig(
-    filename='hackathon-mcp.log',  # Log to a file instead of stdout/stderr
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
+load_dotenv()  # Load environment variables 
 
 import gradio as gr
 import requests
@@ -39,14 +28,14 @@ def search_knowledge_base_for_context(query: str) -> str:
     Returns:
         str: Relevant text content that can be used by the LLM to answer the query.
     """
-    logger.info(f"Searching knowledge base for RAG context via modal labs: {query}")
+    print(f"Searching knowledge base for RAG context via modal labs: {query}")
 
     data = {"query": query}
     modal_url = os.getenv("MODAL_LABS_HACKATHON_RAG_TOOLS_URL")
-    response = requests.post(modal_url, json=data, timeout=1000.0)
-    logger.info(f"modal RAG Response: {response}")
+    response = requests.post(modal_url, json=data, timeout=600.0)
+    print(f"modal RAG Response: {response}")
     if response.status_code != 200:
-        logger.error(f"Error in modal RAG response: {response.status_code} - {response.text}")
+        print(f"Error in modal RAG response: {response.status_code} - {response.text}")
         return "Error in retrieving context from the knowledge base."
     return response.text or "No relevant information found"
 
@@ -71,14 +60,14 @@ def research_write_review_topic(query: str) -> str:
     Returns:
         str: A nicely formatted string.
     """
-    logger.info(f"Researching the topic via modal labs: {query}")
+    print(f"Researching the topic via modal labs: {query}")
 
     data = {"query": query}
     modal_url = os.getenv("MODAL_LABS_HACKATHON_RESEARCH_TOOLS_URL")
-    response = requests.post(modal_url, json=data, timeout=1000.0)
-    logger.info(f"modal RESEARCH Response: {response}")
+    response = requests.post(modal_url, json=data, timeout=600.0)
+    print(f"modal RESEARCH Response: {response}")
     if response.status_code != 200:
-        logger.error(f"Error in modal RESEARCH response: {response.status_code} - {response.text}")
+        print(f"Error in modal RESEARCH response: {response.status_code} - {response.text}")
         return "Error in retrieving research topic."
     return response.text or "Research completed, but no content was generated."
 
@@ -121,7 +110,7 @@ with gr.Blocks() as server_info:
 mcp_rag_tool = gr.Interface(
     fn=search_knowledge_base_for_context,
     inputs=["text"],
-    outputs=[gr.Textbox(label="Knowledge Base", max_lines=10)],
+    outputs=[gr.Textbox(label="Knowledge Base", max_lines=15)],
     title="MCP RAG Tool",
     description="Searches and retrieves relevant context from a knowledge base"
 )
@@ -129,10 +118,10 @@ mcp_rag_tool = gr.Interface(
 research_tool = gr.Interface(
     fn=research_write_review_topic,
     inputs=["text"],
-    outputs=[gr.Textbox(label="Reviewed Topic", max_lines=10)],
+    outputs=[gr.Textbox(label="Reviewed Topic", max_lines=15)],
     title="Research Tool",
     description="Helps with report writing with research, writing, and review agents on any topic. ",
-    concurrency_limit=10
+    concurrency_limit=1
 )
 
 named_interfaces = {
@@ -153,7 +142,7 @@ mcp_server = gr.TabbedInterface(
 
 # Launch the MCP Server
 if __name__ == "__main__":
-    mcp_server.queue(default_concurrency_limit=10)
+    mcp_server.queue(default_concurrency_limit=1)
     mcp_server.launch(
         server_name="0.0.0.0",
         server_port=7860,