Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import pandas as pd
 import pickle
 import os
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
+from llama_index.core import StorageContext, load_index_from_storage, ChatPromptTemplate
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from dotenv import load_dotenv
@@ -19,7 +19,7 @@ with open('feature_columns.pkl', 'rb') as col_file:
     feature_columns = pickle.load(col_file)
 
 # Configure the Llama index settings for the PDF Reader
-HuggingFaceInferenceAPI = HuggingFaceInferenceAPI(
+hf_api = HuggingFaceInferenceAPI(
     model_name="google/gemma-1.1-7b-it", 
     tokenizer_name="google/gemma-1.1-7b-it",
     context_window=3000,
@@ -27,7 +27,7 @@ HuggingFaceInferenceAPI = HuggingFaceInferenceAPI(
     max_new_tokens=512,
     generate_kwargs={"temperature": 0.1}
 )
-HuggingFaceEmbedding = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+hf_embedding = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
 
 # Define the directory for persistent storage and data
 PERSIST_DIR = "./db"
@@ -165,11 +165,10 @@ with gr.Blocks() as demo:
     # Define button actions
     predict_button.click(fn=predict_status, 
                          inputs=[air_temp, proc_temp, speed, torque, tool_wear], 
-                         outputs=[output_status, gr.Variable("reasons")])
+                         outputs=[output_status])
 
-    # Correcting the submit_btn action to extract reasons properly
-    submit_btn.click(fn=lambda reasons: handle_query(reasons), 
-                     inputs=gr.Variable("reasons"), 
+    submit_btn.click(fn=handle_query, 
+                     inputs=chat_box, 
                      outputs=response_box)
 
     # Display the PDF content