Update app.py

app.py

@@ -1,33 +1,149 @@
 import tempfile
 import gradio as gr
 import janus_swi as janus
+from crewai import Agent, Task, Crew
+from crewai_tools import tool
+from crewai_tools import MDXSearchTool
+from crewai_tools import WebsiteSearchTool
+from langchain_anthropic import ChatAnthropic
 import nest_asyncio
 
 nest_asyncio.apply()
 
-def yes_man(message, history):
+MODEL_NAME = "claude-3-5-sonnet-20240620"
+llm = ChatAnthropic(model=MODEL_NAME,
+                    temperature=0.2,
+                    max_tokens=4096,)
+
+webs_tool = WebsiteSearchTool(
+    website=DOC_URL,
+    config=dict(
+        llm=dict(
+            provider="anthropic",
+            config=dict(
+                model=MODEL_NAME,
+                temperature=0.2,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="ollama",
+            config=dict(
+                model="mxbai-embed-large",
+                # task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+docs_tool = MDXSearchTool(
+    mdx='agent_doc.md',
+    config=dict(
+        llm=dict(
+            provider="anthropic",
+            config=dict(
+                model=MODEL_NAME,
+                temperature=0.2,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="ollama",
+            config=dict(
+                model="mxbai-embed-large",
+                # task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+
+@tool("Prolog Query Engine")
+def prolog_query_engine(code: str, query: str) -> str:
+    """Executes a Prolog query with additional Prolog code defining predicates and facts, and returns the results.
+
+    Args:
+        code: Prolog code defining predicates and facts. This code will be appended to knowledge base before executing the query.
+        query: The Prolog query to execute.
+
+    Returns:
+        A string containing the results of the query, with each result on a new line. If the query fails, returns "False".
+    """
     janus.consult("knowledge_base.pl")
-    # tmp = tempfile.NamedTemporaryFile(suffix='.pl')
-
-    # Open the file for writing.
-    # with open(tmp.name, 'w') as f:
-    with open("tmp.pl", 'w') as f:
-        f.write("""% Define the person
-us_citizen(john_doe)
-lawfully_residing(john_doe, 'U.S.', date(1996, 1, 1))
-condition(john_doe, 'Blind')""")
-    # janus.consult(tmp.name)
+
+    # Remove code block markers if present
+    if '```' in code:
+        code = code.split('```')[1].split('```')[0]
+
+    # Write the provided Prolog code to a temporary file
+    with open('tmp.pl', 'w') as f:
+        f.write(code)
+
+    # Consult the temporary file to load the provided Prolog code
     janus.consult("tmp.pl")
-    if message.endswith("?"):
-        result = str(janus.query_once("eligible_for_ssi(john_doe)"))
+
+    # Execute the query and return the results
+    results = janus.query(query)
+    if results:
+        return '\n'.join([str(r) for r in results])
     else:
-        result = '\n- '.join([str(r) for r in janus.query("eligible_for_ssi(john_doe)")])
-    # tmp.close()
-    return result
+        return "False"
+
+# Define your agents with roles, goals, and tools
+programmer = Agent(
+    role='Software Engineer',
+    goal='Write Prolog code and a line of Prolog queries to answer user queries',
+    backstory='''A software engineer with expertise in logic programming and experience using Prolog. 
+    Can translate user requests into Prolog code and execute queries to provide accurate results. 
+    Familiar with various Prolog concepts like recursion, backtracking, and unification.''',
+    tools=[prolog_query_engine, docs_tool],
+    llm=llm
+)
+consultant = Agent(
+    role='Consultant',
+    goal='Answer user query and explain in simple English that even 8 year old kid can understand',
+    backstory='''A friendly and patient consultant, skilled at explaining complex topics in a clear and simple way. 
+    Can understand the output of a software engineer and translate it into easy-to-understand explanations, 
+    even for someone as young as eight years old. Use simple words and examples to make learning fun and engaging.''',
+    tools=[webs_tool],
+    llm=llm
+)
+
+# Define a task
+task1 = Task(
+    name='Answer user query',
+    description='Given a user query, write Prolog defining predicates and facts in query and build a Prolog query to access knowledge base and answer user query.\nUser query: {query}',
+    agent=programmer,
+    expected_output='''A report including:\n\
+    - User query\n\
+    - Prolog code with predicates and facts\n\
+    - Prolog query used to answer the user query\n\
+    - Result of running the Prolog query\n\
+    - A basic explanation of the result, clarifying how the Prolog query produced the answer'''
+)
+# Define a task
+task2 = Task(
+    name='Reply user query',
+    description='Given answer to user query, improve the wordings in answer using your knowledge.',
+    agent=consultant,
+    expected_output='A clear, concise, and easy-to-understand explanation of the answer to the user query, suitable for an 8-year-old.'
+)
+
+# Create a crew
+crew = Crew(
+    agents=[programmer, consultant],
+    tasks=[task1, task2],
+    verbose=True)
+
+
+def yes_man(user_query, history):
+    return crew.kickoff(inputs={"query": user_query})
 
 gr.ChatInterface(
     yes_man,
-    title="Yes Man",
-    description="Ask Yes Man any question",
-    examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
-).launch()
+    title="SSI/SSDI expert",
+    description="Ask expert system any question",
+    examples=["Is it eligible for a blind US citizen born in 1996 Jan 2 name John Doe to get SSI?"],
+).queue().launch()