Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,54 @@
+from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
+from langchain import OpenAI
+from langchain import LLMChain, PromptTemplate
+from langchain.memory import ConversationBufferMemory
+import sys
+import os
+import gradio as gr
+from IPython.display import Markdown, display
+
+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 prompt helper
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+
+    # define LLM
+    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+
+    index.save_to_disk('index.json')
+
+    return index
+
+def ask_ai(query):
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    response = index.query(query)
+    return f"Response: *{response.response}*"
+
+OPENAI_API_KEY="OPENAI_API_KEY"
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+construct_index("chatbotdata")
+
+iface = gr.Interface(
+    fn=ask_ai,
+    inputs="text",
+    outputs=gr.outputs.Textbox(),
+    layout="vertical",
+    title="Ask AI",
+    description="Ask the AI any question.",
+)
+
+iface.launch()