Update app.py

agent.py

@@ -47,7 +47,7 @@ from langchain.agents import initialize_agent, AgentType
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.tools import Tool
 import time
-from langchain_mistralai.chat_models import ChatMistralAI
+from huggingface_hub import InferenceClient
 
 load_dotenv()
 
@@ -450,7 +450,38 @@ tools = [wiki_tool, calc_tool, file_tool, web_tool, arvix_tool, youtube_tool, vi
 
 # Define the LLM before using it
 #llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") # or "gpt-3.5-turbo" "gpt-4"
-llm = ChatMistralAI(model="mistral-7b-instruct-v0.1")
+#llm = ChatMistralAI(model="mistral-7b-instruct-v0.1")
+
+from transformers import pipeline
+
+# Get the Hugging Face API token from the environment variable
+# Get the Hugging Face API token from the environment variable
+hf_token = os.getenv("HF_TOKEN")
+
+# Load the Qwen2.5-Coder-32B-Instruct model using HuggingFaceHub
+qwen_model = HuggingFaceHub(
+    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",  # Specify the model from Hugging Face
+    api_key=hf_token,  # Pass the API token for access
+    model_kwargs={"temperature": 0.7}  # Adjust temperature as needed
+)
+
+# Example usage with LangChain
+tools = [
+    Tool(
+        name="code_tool",
+        func=qwen_model.run,  # Use the run method for inference
+        description="Use this tool for code generation or similar tasks"
+    )
+]
+
+# Initialize the LangChain agent with the tool(s) and the model
+agent = initialize_agent(
+    tools=tools,
+    llm=qwen_model,
+    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    verbose=True
+)
+
 
 # Create an agent using the planner, task classifier, and decision logic
 agent = initialize_agent(