Update app.py

app.py

@@ -25,36 +25,48 @@ from langchain import OpenAI
 from IPython.display import Markdown, display
 import gradio as gr
 import gradio
+import pandas as pd
+from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain import OpenAI
+import sys
+import os
+from IPython.display import Markdown, display
+import pandas as pd
+from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain import OpenAI
+from IPython.display import Markdown, display
+import streamlit as st
+import pickle
 df = pd.read_excel('Shegardi_dataset.xlsx',sheet_name = 'dataset')
-os.environ['OPENAI_API_KEY'] = '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'
+os.environ['OPENAI_API_KEY'] = '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'
+
 def construct_index(directory_path):
-    # set maximum input size
     max_input_size = 4096
-    # set number of output tokens
     num_outputs = 2000
-    # set maximum chunk overlap
     max_chunk_overlap = 20
-    # set chunk size limit
     chunk_size_limit = 600 
 
-    # define LLM
     llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
     prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
- 
+
     documents = SimpleDirectoryReader(directory_path).load_data()
-    
+
     index = GPTSimpleVectorIndex(
         documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
     )
 
     index.save_to_disk('index.json')
 
-    return index
+    with open('llm_predictor.pkl', 'wb') as f:
+        pickle.dump(llm_predictor, f)
 
-#construct_index("context_data/data")
+    return index
 
 def ask_ai(query):
-    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    with open('llm_predictor.pkl', 'rb') as f:
+        llm_predictor = pickle.load(f)
+
+    index = GPTSimpleVectorIndex.load_from_disk('index.json', llm_predictor=llm_predictor)
     response = index.query(query, response_mode="compact")
     return response.response
 
@@ -68,3 +80,9 @@ iface.launch(share=True)
 
 
 
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