Update app.py

app.py

@@ -23,60 +23,49 @@ 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 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
+#import gradio
 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')
 
-    #with open('llm_predictor.pkl', 'wb') as f:
-        #pickle.dump(llm_predictor, f)
-
     return index
 
-def ask_ai(query):
-    #with open('llm_predictor.pkl', 'rb') as f:
-        #llm_predictor = pickle.load(f)
+#construct_index("context_data/data")
+
+#import streamlit as st
+# Include other necessary imports here
 
-    index = GPTSimpleVectorIndex.load_from_disk('index.json', llm_predictor=llm_predictor)
+def ask_ai(query):
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
     response = index.query(query, response_mode="compact")
     return response.response
-
+    
 iface = gr.Interface(fn=ask_ai, inputs="text", outputs="text", title="The following is a conversation with a human called Shegardi. Shegardi is helpful, precise, truthful, and very friendly.  Also, Shegardi is an employee of Warba Bank, located in Kuwait. Shegardi will only use the information provided to him. ", 
                      description="Enter a question and get an answer from Shegardi.")
-iface.launch()
-
-
-
-
+iface.launch(share=True)