Create app.py

app.py

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+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
+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=0.4, model_name="text-davinci-003", max_tokens=num_outputs))
+    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor,  prompt_helper=prompt_helper)
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) #, 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=7, label="Enter your text"),
+                     outputs="text",
+                     title="Tikehau-trained AI Chatbot")
+
+index = construct_index("docs")
+iface.launch(share=True)
+# Press the green button in the gutter to run the script.
+# if __name__ == '__main__':
+#     index = construct_index("docs")
+#     iface.launch(share=True)
+
+