Update app.py

app.py

@@ -1,11 +1,12 @@
 """
 This file is the main file for the hackathon.
-It contains the Gradio interface for the hackathon as a MCP server.
+It contains the Gradio interface for the hackathon as an MCP server.
 It exposes the following tools:
 - search_knowledge_base_for_context
 - research_write_review_topic
-
 """
+
+import os
 from dotenv import load_dotenv
 load_dotenv()  # Load environment variables from .env file
 
@@ -21,12 +22,11 @@ logging.basicConfig(
 logger = logging.getLogger(__name__)
 
 import gradio as gr
-from tools.rag_tools import search_groundx_for_rag_context
-from tools.multi_agent_workflow_for_research import run_research_workflow
+import requests
 
 def search_knowledge_base_for_context(query: str) -> str:
     """
-    Searches and retrieves relevant context from a knowledge base (GroundX),
+    Searches and retrieves relevant context from a knowledge base on Sukhoi SU-35,
     based on the user's query.
 
      Example queries:
@@ -39,10 +39,16 @@ 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 document for RAG context {query}")
-    response = search_groundx_for_rag_context(query)
-    logger.info(f"RAG Response: {response}")
-    return response
+    logger.info(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}")
+    if response.status_code != 200:
+        logger.error(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"
 
 def research_write_review_topic(query: str) -> str:
     """
@@ -65,46 +71,51 @@ def research_write_review_topic(query: str) -> str:
     Returns:
         str: A nicely formatted string.
     """
-    try:
-        logger.info(f"Researching the topic: {query}")
-        result = run_research_workflow(query)
-        return result or "Research completed, but no content was generated."
-    except Exception as e:
-        return f"Error: {e}"
+    logger.info(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}")
+    if response.status_code != 200:
+        logger.error(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."
 
 with gr.Blocks() as server_info:
     gr.Markdown("""
-    # MCP-Powered RAG and Research Topic
+        # 👁 MCP Powered RAG and Research Topic 🌍
 
-    I present to you an MCP-powered RAG and Research.
+        I present to you an MCP-powered RAG and Research Topic.
+        The Tools are hosted and executed on **Modal Labs**
 
-    RAG Tool uses the knowledge base stored on the **GroundX by eyelevel.ai**. The knowledge base is a document that contains information about the SU-35 aircraft, including its features, capabilities, and specifications.
-    Please check [this PDF](https://airgroup2000.com/gallery/albums/userpics/32438/SU-35_TM_eng.pdf) to formulate queries on Sukhoi.
+        RAG Tool uses the **GroundX** storage by **eyelevel.ai** to fetch the knowledge base. The knowledge base is a document that contains information about the SU-35 aircraft, including its features, capabilities, and specifications. Please check [this PDF](https://airgroup2000.com/gallery/albums/userpics/32438/SU-35_TM_eng.pdf) to formulate queries on Sukhoi.
 
-    The Research Tool is implemented using multi-agent workflow using **LlamaIndex** (ResearchAgent, WriteAgent, and ReviewAgent).
-    The Agents uses LLM provided by **Nebius**
+        The Research Tool is implemented using a multi-agent workflow using **LlamaIndex** (ResearchAgent, WriteAgent, and ReviewAgent).
+        <br>
+        The Agents use **Nebius** provided LLM.
 
-    ## Available Tools
+        ## Available Tools
 
-    ### search_knowledge_base_for_context
-    - **Description**: Searches and retrieves relevant context from a knowledge base based on the user's query.
-    - **Example Queries**:
-        - "What are the main features of fuel system of SU-35."
-        - "What are the combat potential of SU-35."
+        ### search_knowledge_base_for_context
+        - **Description**: Searches and retrieves relevant context from a knowledge base based on the user's query.
+        - **Example Queries**:
+            - "What are the main features of fuel system of SU-35?"
+            - "What is the combat potential of SU-35?"
 
-    ### research_write_review_topic
-    - **Description**: Helps with writing a report with research, writing, and review on any topic.
-    - **Example Queries**:
-        - "Write me a report on the history of the internet."
-        - "Write me a report on origin of the universe."
-        - "Write me a report on the impact of climate change on polar bears."
+        ### research_write_review_topic
+        - **Description**: Helps with writing a report with research, writing, and review on any topic.
+        - **Example Queries**:
+            - "Write me a report on the history of the internet."
+            - "Write me a report on origin of the universe."
+            - "Write me a report on the impact of climate change on polar bears."
 
-    ## How to Use
-    - Use the MCP RAG Tool tab above to query the knowledge base.
-    - Use the Research Tool tab above to write report on any topic.
+        ## How to Use
+        - Use the MCP RAG Tool tab above to query the knowledge base.
+        - Use the Research Tool tab above to write a report on any topic.
 
-    ## Watch the Demo Video here
-    [Link to Demo on Youtube](https://youtu.be/wvHBqW2ABGg)
+        ## Watch the Demo Video here
+        [Link to Demo on Youtube](https://youtu.be/wvHBqW2ABGg)
     """)
 
 mcp_rag_tool = gr.Interface(