Create app.py

app.py

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+# for hugingface to create app.py
+# !pip install -U gradio pinecone llama-index llama-index-vector-stores-pinecone llama-index-readers-file pypdf
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings
+# --- Imports ---
+import logging
+import sys
+import gradio as gr
+
+from google.colab import userdata
+from pinecone import Pinecone, ServerlessSpec
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext , Settings
+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)
+
+#load keys for huggingface
+import os
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+
+
+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
+
+# Define a system prompt
+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.
+
+- If a question pertains to topics outside DDS HR policies, respond politely, clarifying that you are a human resources bot and only answer DDS HR questions.
+- For questions you cannot answer (e.g., requests for old policies, salary details, or confidential information), politely decline and direct the user to email connect@decodingdatascience.com.
+- Never answer questions about anything outside of your scope.
+- Persist in following these constraints for any follow-up questions.
+- Before answering, carefully check that the information and query are within the allowed scope. Follow chain-of-thought reasoning:
+  1. First, reason step-by-step whether the question is covered in the current handbook and is within HR.
+  2. Only after confirming, produce a final answer.
+
+Format answers as concise, professional responses. Do not wrap answers in code blocks or any special formatting.
+
+Output requirements:
+- For allowed HR questions, answer concisely based only on the latest DDS HR handbook information.
+- For forbidden topics, output: “I’m sorry, I can only answer questions about the latest DDS HR policies. For confidential or other queries, please email connect@decodingdatascience.com.”
+
+
+**Example 1**
+User: What is the leave encashment policy at DDS?
+Reasoning: This is an HR policy question found in the latest handbook.
+Final Answer: [Provide answer summarized from the latest handbook’s section on leave encashment]
+
+**Example 2**
+User: Can you tell me the salary range for Data Scientists?
+Reasoning: Salary details are confidential and not shared by this bot.
+Final Answer: I’m sorry, I can only answer questions about the latest DDS HR policies. For confidential or other queries, please email connect@decodingdatascience.com.
+
+**Example 3**
+User: Can you explain what DDS does as a company overall?
+Reasoning: This is not an HR question, so it cannot be answered.
+Final Answer: I’m sorry, I only answer DDS HR policy questions as outlined in the handbook.
+
+(Real-world examples should be longer and use precise wording from the handbook where appropriate.)
+
+**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.
+
+'''
+
+
+
+
+# --- Initialize Pinecone ---
+pc = Pinecone(api_key=PINECONE_API_KEY)
+index_name = "quickstart"
+dimension = 1536
+
+# --- Delete index if it already exists (optional) ---
+existing_indexes = [idx["name"] for idx in pc.list_indexes()]
+
+if index_name in existing_indexes:
+    pc.delete_index(index_name)
+
+# --- Create Pinecone index ---
+pc.create_index(
+    name=index_name,
+    dimension=dimension,
+    metric="euclidean",
+    spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+)
+
+pinecone_index = pc.Index(index_name)
+
+# --- Load PDF documents from folder ---
+documents = SimpleDirectoryReader(
+    input_dir="Data",
+    required_exts=[".pdf"],
+    file_extractor={".pdf": PDFReader()}
+).load_data()
+
+if not documents:
+    raise ValueError("No PDF documents were loaded from the 'data' folder.")
+
+# --- 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
+)
+
+# --- Query Engine ---
+query_engine = index.as_query_engine(system_prompt=system_prompt)
+
+# --- Gradio App ---
+def query_doc(prompt):
+    try:
+        response = query_engine.query(prompt)
+        return str(response)
+    except Exception as e:
+        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",
+    description="Ask questions related to HR for latest Information."
+).launch(share=True)
+
+
+
+
+
+
+