Add application file and requirements

app.py

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+import gradio as gr
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+import fitz  # PyMuPDF
+from datasets import load_dataset
+from llama_index.core import Document, VectorStoreIndex, StorageContext, load_index_from_storage, Settings
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.ollama import Ollama
+
+# Load Llama 3 model components
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom", passages_path="my_knowledge_base.faiss")
+model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
+
+# Load the embedding model
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+# Create an LLM object using the deployed Llama3 Ollama instance
+llm = Ollama(model="llama3:instruct", request_timeout=60.0)
+
+# Set global settings for the LLM, chunk size, and embedding model
+Settings.llm = llm
+Settings.chunk_size = 512
+Settings.embed_model = embed_model
+
+# Function to extract text from PDFs
+def extract_text_from_pdf(pdf_files):
+    texts = []
+    for pdf in pdf_files:
+        doc = fitz.open(pdf.name)
+        text = ""
+        for page in doc:
+            text += page.get_text()
+        texts.append(text)
+    return texts
+
+# Function to provide answers based on questions and PDFs
+def rag_answer(question, pdf_files):
+    texts = extract_text_from_pdf(pdf_files)
+    context = " ".join(texts)
+    inputs = tokenizer(question, return_tensors="pt")
+    outputs = model.generate(**inputs, context_input=context)
+    return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+
+# Function to create the Vector Store Index from documents
+def create_vector_store_index(documents):
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir="pdf_docs")
+    return index
+
+# Load dataset and convert to Document format
+pdf_docs = load_dataset('your-dataset-name', split='train')  # Replace with your actual dataset name
+documents = [Document(text=row['text'], metadata={'title': row['title']}) for index, row in pdf_docs.iterrows()]
+
+# Create or load the vector store index
+try:
+    storage_context = StorageContext.from_defaults(persist_dir="pdf_docs")
+    vector_index = load_index_from_storage(storage_context)
+except:
+    vector_index = create_vector_store_index(documents)
+
+# Define the query engine powered by the Vector Store
+query_engine = vector_index.as_query_engine(similarity_top_k=10)
+
+# Functions for Gradio UI
+def query(text):
+    z = query_engine.query(text)
+    return z
+
+def interface(text):
+    z = query(text)
+    response = z.response
+    return response
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Glass().set(block_title_text_color="black", body_background_fill="black", input_background_fill="black", body_text_color="white")) as demo:
+    gr.Markdown("h1 {text-align: center;display: block;}Information Custodian Chat Agent")
+    with gr.Row():
+        output_text = gr.Textbox(lines=20)
+    with gr.Row():
+        input_text = gr.Textbox(label='Enter your query here')
+    input_text.submit(fn=interface, inputs=input_text, outputs=output_text)
+
+demo.launch(share=True)

requirements.txt

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+transformers
+gradio
+faiss-cpu
+datasets
+PyMuPDF
+llama-index-embeddings-instructor
+llama-index-embeddings-huggingface
+llama-index-llms-ollama
+llama-index