Create app.py

app.py

@@ -0,0 +1,60 @@
+# app.py
+
+import os
+import gradio as gr
+import openai
+import logging
+from pinecone import Pinecone, ServerlessSpec
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
+from llama_index.vector_stores.pinecone import PineconeVectorStore
+
+# 1. Configure Logging
+logging.basicConfig(level=logging.INFO)
+
+# 2. Set API Keys (Use environment variables for security)
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+os.environ["PINECONE_API_KEY"] = "your-pinecone-api-key"
+
+# 3. Initialize Pinecone
+pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
+index_name = "quickstart"
+
+# Delete & Create Index if needed
+if index_name in [idx["name"] for idx in pc.list_indexes()]:
+    pc.delete_index(index_name)
+
+pc.create_index(
+    name=index_name,
+    dimension=1536,
+    metric="euclidean",
+    spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+)
+
+pinecone_index = pc.Index(index_name)
+
+# 4. Load Documents
+documents = SimpleDirectoryReader("./data/paul_graham").load_data()
+
+# 5. Create Vector Index
+vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+
+# 6. Create Query Engine
+query_engine = index.as_query_engine()
+
+# 7. Gradio Interface
+def query_document(user_query):
+    response = query_engine.query(user_query)
+    return str(response)
+
+# 8. Gradio App
+interface = gr.Interface(
+    fn=query_document,
+    inputs=gr.Textbox(label="Enter your query", placeholder="Ask something about the essay..."),
+    outputs=gr.Textbox(label="Response"),
+    title="Ask Paul Graham (Powered by LlamaIndex + Pinecone)"
+)
+
+if __name__ == "__main__":
+    interface.launch()