init

app.py

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+import os
+import gradio as gr
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import (
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    StorageContext,
+    load_index_from_storage, Settings,
+)
+from llama_index.llms.ollama import Ollama
+
+
+# Path to your local corpus directory
+PERSIST_DIR = './storage'
+
+# Configure the settings
+Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
+Settings.llm = Ollama(model="llama3", request_timeout=360.0)
+
+# Load the existing index
+storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+index = load_index_from_storage(storage_context)
+
+query_engine = index.as_query_engine()
+
+def chatbot_response(user_input):
+    response = query_engine.query(user_input)
+    return str(response)
+
+# Create a Gradio interface
+interface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text", title="Chatbot")
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()