Create app.py

app.py

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+# coding=utf8
+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain import OpenAI
+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=OpenAI(temperature=0.7, model_name="text-davinci-003", 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')
+    response = index.query(input_text, response_mode="compact")
+    return response.response
+
+
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.inputs.Textbox(lines=3, label="O quê gostaria de saber?"),
+                     outputs=gr.inputs.Textbox(lines=3, label="Veja o que encontrei"),
+                     title="CD2 IA")
+
+index = construct_index("docs")
+iface.launch(share=True)