Create app.py

app.py

@@ -0,0 +1,45 @@
+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()