Create app.py

app.py

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+#### This for hugging face space for zero gpu
+##1. create requirements.txt
+##2.add keys in HF secrets
+##3. put this in app.py
+# --- Imports ---
+import logging
+import os
+import sys
+
+import gradio as gr
+import spaces
+
+from pinecone import Pinecone, ServerlessSpec
+from llama_index.core import (
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    StorageContext,
+    Settings,
+    PromptTemplate,
+)
+from llama_index.vector_stores.pinecone import PineconeVectorStore
+from llama_index.readers.file import PDFReader
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+
+# --- Logging ---
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# --- Keys (set these in Space Settings -> Variables and secrets) ---
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+
+
+
+# --- LlamaIndex global settings ---
+Settings.llm = OpenAI(model="gpt-4o-mini", temperature=0.2)
+Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
+Settings.chunk_size = 600
+Settings.chunk_overlap = 200
+
+SYSTEM_PROMPT = """You are AYesha, the Decoding Data Science (DDS) Enterprise HR Chatbot. Answer questions exclusively using the attached DDS HR Handbook. Base all responses on the most up-to-date information available in the handbook. Only respond to queries directly related to DDS HR policies as outlined in the handbook.
+Important instructions:
+- Only answer questions directly supported by the latest DDS HR handbook.
+- Decline politely and redirect to the provided email address for any questions outside scope or for confidential information.
+- Always reason before concluding. Only present the answer after checking scope and source.
+Remember: As AYesha, the DDS HR Enterprise Chatbot, you must never provide information outside authorized HR handbook content and always respond respectfully according to these constraints.
+"""
+
+# LlamaIndex's query engine doesn't accept a `system_prompt=` kwarg — that param only
+# exists on as_chat_engine(). The equivalent for a query engine is a custom QA prompt
+# template, which is what actually enforces AYesha's scope/persona.
+QA_TEMPLATE = PromptTemplate(
+    SYSTEM_PROMPT
+    + "\n\nContext information is below.\n---------------------\n{context_str}\n---------------------\n"
+    "Given the context information and not prior knowledge, answer the query.\n"
+    "Query: {query_str}\nAnswer: "
+)
+
+# --- Initialize Pinecone ---
+pc = Pinecone(api_key=PINECONE_API_KEY)
+INDEX_NAME = "quickstart"
+DIMENSION = 1536
+
+existing_indexes = [idx["name"] for idx in pc.list_indexes()]
+
+if INDEX_NAME not in existing_indexes:
+    # Only create the index the first time. Deleting + recreating on every
+    # restart re-embeds every PDF (real API cost) and risks a race if two
+    # cold starts overlap — so we only build the index when it doesn't exist yet.
+    pc.create_index(
+        name=INDEX_NAME,
+        dimension=DIMENSION,
+        metric="euclidean",
+        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+    )
+
+pinecone_index = pc.Index(INDEX_NAME)
+vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
+
+# If the index is already populated (from a prior run), just attach to it.
+# Otherwise load the PDFs and build it now.
+stats = pinecone_index.describe_index_stats()
+if stats.get("total_vector_count", 0) > 0:
+    index = VectorStoreIndex.from_vector_store(vector_store)
+else:
+    documents = SimpleDirectoryReader(
+        input_dir="Data",  # folder name is case-sensitive — must match exactly what you upload
+        required_exts=[".pdf"],
+        file_extractor={".pdf": PDFReader()},
+    ).load_data()
+
+    if not documents:
+        raise ValueError(
+            "No PDF documents were loaded from the 'Data' folder. "
+            "Make sure a folder named exactly 'Data' with your PDF(s) is uploaded alongside app.py."
+        )
+
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+
+query_engine = index.as_query_engine(text_qa_template=QA_TEMPLATE)
+
+
+# --- Gradio App ---
+@spaces.GPU
+def query_doc(prompt):
+    try:
+        response = query_engine.query(prompt)
+        return str(response)
+    except Exception as e:
+        logger.exception("Query failed")
+        return f"Error: {str(e)}"
+
+
+gr.Interface(
+    fn=query_doc,
+    inputs=gr.Textbox(label="Ask a question about the document"),
+    outputs=gr.Textbox(label="Answer"),
+    title="DDS Enterprise HR Chatbot — AYesha",
+    description="Ask questions related to HR for latest information.",
+).launch()