Update app.py

app.py

@@ -151,6 +151,7 @@ with gr.Blocks() as demo:
 # Launch the Gradio application
 demo.launch()
 '''
+'''
 from typing import Annotated, Sequence, TypedDict
 import operator
 import functools
@@ -328,6 +329,143 @@ try:
     print(f"[TRACE] Workflow Result: {result}")  # Final workflow result
 except Exception as e:
     print(f"[ERROR] Workflow execution failed: {e}")
+'''
+
+from typing import Annotated, Dict, Any
+import functools
+from langgraph.graph import StateGraph, END
+from langchain_core.messages import HumanMessage
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_experimental.tools import PythonREPLTool
+from langchain_huggingface import HuggingFacePipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import gradio as gr
+
+# Define the agents and tools
+def create_llm():
+    """Create the HuggingFace LLM pipeline."""
+    model_name = "Qwen/Qwen2.5-7B-Instruct-1M"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    
+    llm_pipeline = pipeline(
+        task="text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        device_map="auto",
+        max_new_tokens=500,
+    )
+    return HuggingFacePipeline(pipeline=llm_pipeline)
+
+# Mock Tools for Registration and Scheduling
+registration_tool = TavilySearchResults(max_results=1)  # A mock registration tool
+scheduling_tool = PythonREPLTool()  # A mock scheduling tool
+
+# Define the agents
+def registration_agent(visitor_details: Dict[str, str]) -> Dict[str, Any]:
+    """Check if the visitor is registered."""
+    visitor_name = visitor_details.get("visitor_name")
+    visitor_mobile = visitor_details.get("visitor_mobile")
+
+    # Mock registration lookup (replace this with actual database/API call)
+    registered_visitors = [{"visitor_name": "John Doe", "visitor_mobile": "1234567890"}]
+
+    # Check if visitor exists
+    is_registered = any(
+        v["visitor_name"] == visitor_name and v["visitor_mobile"] == visitor_mobile
+        for v in registered_visitors
+    )
+    return {"registered": is_registered}
+
+def scheduling_agent(scheduling_details: Dict[str, str]) -> Dict[str, str]:
+    """Schedule an appointment."""
+    doctor_name = scheduling_details.get("doctor_name")
+    department_name = scheduling_details.get("department_name")
+
+    # Mock scheduling logic (replace with actual logic/API integration)
+    appointment_status = "Scheduled successfully"
+    return {"status": appointment_status, "doctor_name": doctor_name, "department_name": department_name}
+
+
+# Define Langgraph States
+class VisitorState:
+    messages: Annotated[list, functools.partial(lambda x: [])]
+
+
+def input_state(state):
+    """State to input visitor details."""
+    return {"messages": [HumanMessage(content="Please provide your name and mobile number.")]}
+
+def registration_state(state):
+    """State to check visitor registration."""
+    visitor_name = state["visitor_name"]
+    visitor_mobile = state["visitor_mobile"]
+    result = registration_agent({"visitor_name": visitor_name, "visitor_mobile": visitor_mobile})
+
+    if result["registered"]:
+        return {"messages": ["Visitor is registered."], "next": "SchedulingState"}
+    else:
+        return {"messages": ["Visitor not found in records. Please register first."], "next": END}
+
+def scheduling_state(state):
+    """State for scheduling appointment."""
+    doctor_name = state["doctor_name"]
+    department_name = state["department_name"]
+    result = scheduling_agent({"doctor_name": doctor_name, "department_name": department_name})
+
+    return {
+        "messages": [f"Appointment {result['status']} with Dr.{result['doctor_name']} in {result['department_name']} department."],
+        "next": END
+    }
+
+
+# Build Langgraph Workflow
+workflow = StateGraph(VisitorState)
+
+# Add nodes
+workflow.add_node("InputState", input_state)
+workflow.add_node("RegistrationState", registration_state)
+workflow.add_node("SchedulingState", scheduling_state)
+
+# Define edges
+workflow.add_edge("InputState", "RegistrationState")
+workflow.add_edge("RegistrationState", "SchedulingState")
+workflow.add_conditional_edges(
+    "RegistrationState",
+    lambda x: x.get("next"),
+    {"SchedulingState": "SchedulingState", END: END}
+)
+
+workflow.set_entry_point("InputState")
+compiled_graph = workflow.compile()
+
+# Gradio Frontend
+def gradio_interface(visitor_name, visitor_mobile, doctor_name, department_name):
+    """Gradio interface to interact with the workflow."""
+    state = {
+        "visitor_name": visitor_name,
+        "visitor_mobile": visitor_mobile,
+        "doctor_name": doctor_name,
+        "department_name": department_name,
+    }
+
+    # Invoke workflow
+    result = compiled_graph.invoke(state)
+    return result["messages"][0]
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(label="Visitor Name"),
+        gr.Textbox(label="Visitor Mobile Number"),
+        gr.Textbox(label="Doctor Name"),
+        gr.Textbox(label="Department Name"),
+    ],
+    outputs="textbox",
+)
+
+if __name__ == "__main__":
+    iface.launch()