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app.py

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+
+######################## UPDATED CODE #########################
+
+import gradio as gr
+from datetime import datetime
+from typing import Dict, List
+
+from mem0 import MemoryClient
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain.chat_models import ChatOpenAI
+import json
+
+# Load config from local JSON file
+with open("/content/drive/MyDrive/Generative AI/Project 3/config.json") as f:
+    config = json.load(f)
+
+# Optional: If agentic_rag is in a separate file, import it
+# from agentic_rag_workflow import agentic_rag
+
+# ------------------------ Define NutritionBot ------------------------
+class NutritionBot:
+    def __init__(self):
+        self.memory = MemoryClient(api_key=config.get("API_KEY"))
+
+        self.client = ChatOpenAI(
+            model_name="gpt-4o-mini",
+            api_key=config.get("API_KEY"),
+            endpoint=config.get("OPENAI_API_BASE"),
+            temperature=0
+        )
+
+        tools = [agentic_rag]  # Make sure agentic_rag is defined or imported
+
+        system_prompt = """You are a caring and knowledgeable Medical Support Agent, specializing in nutrition disorder-related guidance. Your goal is to provide accurate, empathetic, and tailored nutritional recommendations while ensuring a seamless customer experience.
+        """
+
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+            ("placeholder", "{agent_scratchpad}")
+        ])
+
+        agent = create_tool_calling_agent(self.client, tools, prompt)
+        self.agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+    def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None):
+        if metadata is None:
+            metadata = {}
+        metadata["timestamp"] = datetime.now().isoformat()
+        conversation = [
+            {"role": "user", "content": message},
+            {"role": "assistant", "content": response}
+        ]
+        self.memory.add(conversation, user_id=user_id, output_format="v1.1", metadata=metadata)
+
+    def get_relevant_history(self, user_id: str, query: str) -> List[Dict]:
+        return self.memory.search(query=query, user_id=user_id, limit=5)
+
+    def handle_customer_query(self, user_id: str, query: str) -> str:
+        relevant_history = self.get_relevant_history(user_id, query)
+        context = "Previous relevant interactions:\n"
+        for memory in relevant_history:
+            context += f"Customer: {memory['memory']}\n"
+            context += f"Support: {memory['memory']}\n"
+            context += "---\n"
+
+        prompt = f"""
+        Context:
+        {context}
+        Current customer query: {query}
+        Provide a helpful response that takes into account any relevant past interactions.
+        """
+
+        response = self.agent_executor.invoke({"input": prompt})
+        self.store_customer_interaction(user_id, query, response["output"], metadata={"type": "support_query"})
+        return response["output"]
+
+# ------------------------ Gradio Interface ------------------------
+bot = NutritionBot()
+
+def respond(user_id, query):
+    return bot.handle_customer_query(user_id, query)
+
+interface = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(label="User ID", placeholder="Enter your name or ID"),
+        gr.Textbox(label="Query", placeholder="Ask about a nutrition disorder...")
+    ],
+    outputs=gr.Textbox(label="Agent Response"),
+    title="Nutrition Disorder Specialist Agent",
+    description="Ask me anything about nutrition-related health issues, treatments, and recommendations!"
+)
+
+interface.launch()