Create app.py

app.py

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+from pathlib import Path
+from llama_index import ServiceContext, download_loader, GPTVectorStoreIndex, LLMPredictor
+from llama_index import StorageContext, load_index_from_storage
+from langchain import OpenAI
+import gradio as gr
+import os
+
+# My personal OpenAI API key - do not misuse :P
+os.environ["OPENAI_API_KEY"] = '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'
+
+num_outputs = 512
+llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.4, model_name="text-davinci-003", max_tokens=num_outputs))
+service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
+
+def construct_index():
+    SimpleCSVReader = download_loader("SimpleCSVReader")
+    loader = SimpleCSVReader()
+    docs = loader.load_data(file=Path('./docs/articles.csv'))
+
+    index = GPTVectorStoreIndex.from_documents(docs, service_context=service_context)
+
+    index.storage_context.persist()
+
+    return index
+
+def chatbot(input_text):
+    storage_context = StorageContext.from_defaults(persist_dir=Path("./storage/"))
+    index = load_index_from_storage(storage_context, service_context=service_context)
+    query_engine = index.as_query_engine(response_mode='compact')
+    response = query_engine.query(input_text)
+    return response.response
+
+# uncomment on first run, when generating an index
+index = construct_index()
+
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.inputs.Textbox(lines=7, label="Enter your text"),
+                     outputs="text",
+                     title="147 AI Chatbot")
+
+
+iface.launch()
+
+# with gr.Blocks() as demo:
+#     chatbot = gr.Chatbot()
+#     msg = gr.Textbox()
+#     clear = gr.Button("Clear")
+
+#     def respond(message, chat_history):
+#         storage_context = StorageContext.from_defaults(persist_dir=Path("./storage/"))
+#         index = load_index_from_storage(storage_context, service_context=service_context)
+#         query_engine = index.as_query_engine(response_mode='compact')
+#         response = query_engine.query(message)
+#         chat_history.append((message, response))
+#         return "", chat_history
+
+#     msg.submit(respond, [msg, chatbot], [msg, chatbot])
+#     clear.click(lambda: None, None, chatbot, queue=False)
+
+# demo.launch()