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Update app.py
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app.py
CHANGED
@@ -1,5 +1,6 @@
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import gradio as gr
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import requests
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from smolagents import CodeAgent
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import logging
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from tenacity import retry, stop_after_attempt, wait_exponential
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@@ -14,10 +15,26 @@ HF_SPACE_URL = "https://manavraj-troubleshoot-mcp.hf.space"
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def call_mcp_server(message: str, tool_type: str = "knowledge_base") -> str:
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"""Call MCP server endpoint"""
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try:
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-
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response.raise_for_status()
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-
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except Exception as e:
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logger.error(f"API call failed: {str(e)}")
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return f"Error: {str(e)}"
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@@ -29,40 +46,49 @@ agent = CodeAgent(
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- requests for API calls
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- standard Python libraries
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{{managed_agents_descriptions}}
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-
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You are a Technical Support Assistant with these capabilities:
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1. Troubleshooting technical issues
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2. Finding information via web search
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3. Formatting instructions
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-
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Access tools through MCP server:
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- knowledge_base: For technical issues
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- web_search: For information lookup
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- formatter: To organize steps
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-
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Response workflow:
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1. Analyze user request
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2. Choose appropriate tool
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3. Return clear response
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-
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Example:
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USER: My wifi disconnected
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THOUGHT: Should use knowledge_base
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ACTION: knowledge_base("wifi disconnection")
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RESPONSE: Try these steps: [solution steps]
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-
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-
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)
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def chat_interface(message: str, history: list) -> str:
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"""Handle chat interaction"""
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try:
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response = agent.run(message)
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except Exception as e:
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logger.error(f"Chat error: {str(e)}")
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return f"Error: {str(e)}"
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demo = gr.ChatInterface(
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fn=chat_interface,
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import gradio as gr
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import requests
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import json
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from smolagents import CodeAgent
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import logging
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from tenacity import retry, stop_after_attempt, wait_exponential
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def call_mcp_server(message: str, tool_type: str = "knowledge_base") -> str:
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"""Call MCP server endpoint"""
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try:
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tool_endpoint_map = {
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"knowledge_base": "predict_1",
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"web_search": "predict_2",
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"formatter": "predict_3"
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}
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endpoint = f"{HF_SPACE_URL}/gradio_api/call/{tool_endpoint_map[tool_type]}/"
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response = requests.post(
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endpoint,
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json={"data": [message]},
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timeout=30,
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headers={"Content-Type": "application/json"}
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)
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response.raise_for_status()
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if response.text.startswith("event: predict"):
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data = json.loads(response.text.split("\n")[1][5:])
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return data["data"][0]
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return response.json()["data"][0]
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except Exception as e:
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logger.error(f"API call failed: {str(e)}")
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return f"Error: {str(e)}"
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- requests for API calls
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- standard Python libraries
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{{managed_agents_descriptions}}
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You are a Technical Support Assistant with these capabilities:
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1. Troubleshooting technical issues
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2. Finding information via web search
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3. Formatting instructions
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Access tools through MCP server:
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- knowledge_base: For technical issues
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- web_search: For information lookup
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- formatter: To organize steps
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Response workflow:
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1. Analyze user request
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2. Choose appropriate tool
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3. Return clear response
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Example:
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USER: My wifi disconnected
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THOUGHT: Should use knowledge_base
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ACTION: knowledge_base("wifi disconnection")
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RESPONSE: Try these steps: [solution steps]
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Important:
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- Always return the full response including ACTION and RESPONSE
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- Never show internal workflow to user
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- If no tool is needed, respond directly"""
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)
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def chat_interface(message: str, history: list) -> str:
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"""Handle chat interaction"""
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try:
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tool_mapping = {
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"knowledge_base": lambda x: call_mcp_server(x, "knowledge_base"),
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"web_search": lambda x: call_mcp_server(x, "web_search"),
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"formatter": lambda x: call_mcp_server(x, "formatter")
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}
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response = agent.run(message)
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if isinstance(response, str):
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if "ACTION:" in response and "RESPONSE:" in response:
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final = response.split("RESPONSE:")[-1].strip()
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return final if final else "I couldn't process that request."
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return response
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return str(response)
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except Exception as e:
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logger.error(f"Chat error: {str(e)}")
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return f"Error processing your request: {str(e)}"
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demo = gr.ChatInterface(
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fn=chat_interface,
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