Delete app.py

app.py

@@ -1,45 +0,0 @@
-import gradio as gr
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, load_index_from_storage
-from llama_index.core import PromptTemplate
-import os
-
-PERSIST_DIR = "./storage"
-if not os.path.exists(PERSIST_DIR):
-    # load the documents and create the index
-    documents = SimpleDirectoryReader("data").load_data()
-    index = VectorStoreIndex.from_documents(documents)
-    # store it for later
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
-else:
-    # load the existing index
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-
-# Query Engine
-query_engine = index.as_query_engine(response_mode="tree_summarize")
-
-new_summary_tmpl_str = (
-    "Context information is below.\n"
-    "---------------------\n"
-    "{context_str}\n"
-    "---------------------\n"
-    "Given the context information and not prior knowledge, "
-    "As an employee specialized in customer service, your main role is to assist users by answering questions based on the context"
-    "You are here to ensure that users receive accurate and helpful responses to their inquiries, making their experience smooth and satisfactory."
-    "If you don't know something, tell the customer that you don't have the information yet and that it will be updated later."
-    "Answer in the same language as the query"
-    "Query: {query_str}\n"
-    "Answer: "
-)
-
-new_summary_tmpl = PromptTemplate(new_summary_tmpl_str)
-query_engine.update_prompts(
-    {"response_synthesizer:summary_template": new_summary_tmpl}
-)
-
-def predict(user_prompt: str) -> str:
-    related_doc = query_engine.query(user_prompt)
-    return related_doc
-
-
-gr.Interface(fn=predict, inputs="text", outputs="text").launch()