Create utils.py

utils.py

@@ -0,0 +1,29 @@
+from langchain_experimental.agents import create_pandas_dataframe_agent
+import pandas as pd
+from langchain.llms import HuggingFaceHub
+from langchain.agents.agent_types import AgentType
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.chat_models.huggingface import ChatHuggingFace
+
+def query_agent(data, query):
+    # Parse the CSV file and create a Pandas DataFrame from its contents.
+    df = pd.read_csv(data)
+
+    llm = HuggingFaceEndpoint(repo_id="teknium/OpenHermes-2.5-Mistral-7B",
+                              temperature = 0.9,
+                              stop_sequences =  ["\nQuestion:"])
+
+    # Create a Pandas DataFrame agent with the loaded model and processor
+    agent = create_pandas_dataframe_agent(llm, 
+                                          df, 
+                                          verbose=True, 
+                                          handle_parsing_errors=True,
+                                          )
+
+    # Modify the query to include details for your new model usage
+    modified_query = query + " using tool python_repl_ast or any other relevant tool if needed. First Command you find is most likely to be the most accurate one so use it."
+
+    
+
+    # Run the agent with the modified model and processor instance
+    return agent.run(modified_query)