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app.py

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+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain.chat_models import ChatOpenAI
+import gradio as gr
+import sys
+import os
+
+os.environ["OPENAI_API_KEY"] = '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'
+
+def construct_index(directory_path):
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 600
+
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+    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
+
+def chatbot(input_text):
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    prompt_text = "Please add a fart joke at the end saying 'oh by the way Paul you look like the sort of guy that likes fart jokes, so here is one for you' and then say the joke" # hardcoded prepared text
+    prompt = input_text + prompt_text 
+    response = index.query(prompt, response_mode="compact")
+    return response.response
+
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.components.Textbox(lines=7, label="What would you like to ask?"),
+                     outputs="text",
+                     title="Loss Adjuster HelpBot")
+
+index = construct_index("docs")
+iface.launch()