Updated app.py

app.py

@@ -1,43 +1,69 @@
 import gradio as gr
-from groq import Groq
 import os
+from langchain_groq import ChatGroq  # Using Groq's API
+from langchain.memory import ConversationBufferMemory
+from langchain.schema import SystemMessage, HumanMessage, AIMessage
+from langchain.agents import initialize_agent, AgentType
+from langchain.tools import Tool
 
-# Set your Groq API key
-API_KEY = os.getenv("API_KEY")
-client = Groq(api_key=API_KEY)  # Pass API key explicitly
+# Set API Key for Groq
+API_KEY = os.getenv("API_KEY")  # Ensure API Key is set in the environment
 
+# Initialize the LLM (Groq's Mixtral)
+llm = ChatGroq(
+    groq_api_key=API_KEY,
+    model_name="mixtral-8x7b-32768",
+    temperature=0.7,
+    max_tokens=512,
+)
+
+# Memory for conversation history
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+# Define useful tools
+def search_tool(query: str) -> str:
+    """A simple search function (can be connected to real APIs)."""
+    return f"Searching for: {query}... [Sample Response]"
+
+tools = [
+    Tool(
+        name="Search Tool",
+        func=search_tool,
+        description="Searches for information based on user queries."
+    )
+]
+
+# Initialize the agent
+agent = initialize_agent(
+    tools=tools,
+    llm=llm,
+    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
+    verbose=True,
+    memory=memory,
+)
+
+# Define response function
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
+    memory.chat_memory.add_message(SystemMessage(content=system_message))
 
     for user_input, bot_response in history:
         if user_input:
-            messages.append({"role": "user", "content": user_input})
+            memory.chat_memory.add_message(HumanMessage(content=user_input))
         if bot_response:
-            messages.append({"role": "assistant", "content": bot_response})
-
-    messages.append({"role": "user", "content": message})
-
-    completion = client.chat.completions.create(
-        model="mixtral-8x7b-32768",
-        messages=messages,
-        temperature=temperature,
-        max_completion_tokens=max_tokens,
-        top_p=top_p,
-        stream=True,
-        stop=None,
-    )
+            memory.chat_memory.add_message(AIMessage(content=bot_response))
+
+    memory.chat_memory.add_message(HumanMessage(content=message))
+
+    response = agent.run(message)
+
+    return response
 
-    response = ""
-    for chunk in completion:
-        token = chunk.choices[0].delta.content or ""
-        response += token
-        yield response
 
 # Gradio Interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly AI assistant.", label="System message"),
+        gr.Textbox(value="You are a helpful Travel AI assistant.Your name is Travelo", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
@@ -45,4 +71,4 @@ demo = gr.ChatInterface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()