Create app.py

app.py

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+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+import faiss
+import pickle
+import torch
+
+from tqdm import tqdm
+from langchain.docstore import InMemoryDocstore
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
+from ragatouille import RAGPretrainedModel
+from typing import List, Tuple, Optional
+from langchain.docstore.document import Document as LangchainDocument
+
+
+# Model initialization
+READER_MODEL_NAME = "RJ1200/llama-3_3b-fine_tuned"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+model = AutoModelForCausalLM.from_pretrained(READER_MODEL_NAME, quantization_config=bnb_config)
+tokenizer = AutoTokenizer.from_pretrained(READER_MODEL_NAME)
+
+READER_LLM = pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    do_sample=True,
+    temperature=0.2,
+    repetition_penalty=1.1,
+    return_full_text=False,
+    max_new_tokens=1000,
+)
+
+# Initialize reranker
+RERANKER = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+
+prompt_in_chat_format = [
+    {
+        "role": "system",
+        "content": """You are an AI assistant specializing in analyzing PDF documents. Your task is to generate a comprehensive question paper based on the provided PDF context.
+        For each section mentioned, generate the exact number of questions as specified.
+        Ensure that the questions are relevant, clear, and cover the key topics within the section.
+        Reference specific page numbers or sections from the PDF whenever applicable.
+        If the information needed to create questions is not available in the PDF context, clearly state that.
+        """,
+    },
+    {
+        "role": "user",
+        "content": """PDF Context:
+        {context}
+        ---
+        For the following sections, generate the required number of questions:
+        section_requirements
+    part A-10,
+    part B- 5,
+    part C- 4
+
+        ---
+        Question: {question}""",
+    },
+]
+
+RAG_PROMPT_TEMPLATE = tokenizer.apply_chat_template(
+    prompt_in_chat_format, tokenize=False, add_generation_prompt=True
+)
+
+def answer_with_rag(
+    question: str,
+    llm: pipeline,
+    knowledge_index: FAISS,
+    reranker: Optional[RAGPretrainedModel] = None,
+    num_retrieved_docs: int = 30,
+    num_docs_final: int = 5,
+) -> Tuple[str, List[str]]:
+    # Gather documents with retriever
+    relevant_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=question, k=num_retrieved_docs)
+    relevant_docs = [doc.page_content for doc in relevant_docs]  # Keep only the text
+
+    # Optionally rerank results
+    if reranker:
+        relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)
+        relevant_docs = [doc["content"] for doc in relevant_docs]
+        relevant_docs = relevant_docs[:num_docs_final]
+
+    # Build the final prompt
+    context = "\nExtracted PDF content:\n"
+    context += "".join([f"Section {str(i+1)}:::\n" + doc for i, doc in enumerate(relevant_docs)])
+    final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)
+
+    # Generate an answer
+    answer = llm(final_prompt)[0]["generated_text"]
+    return answer, relevant_docs
+
+def generate_questions(context: str):
+    question = "generate end-sem question paper?"
+    answer, relevant_docs = answer_with_rag(question, READER_LLM, KNOWLEDGE_VECTOR_DATABASE, reranker=RERANKER)
+    return answer
+
+# Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("""
+    # C Question Paper Generator
+    
+    """)
+
+    with gr.Row():
+        context_input = gr.Textbox(label="Enter Prompt", placeholder="prompt", lines=1)
+
+    generate_button = gr.Button("Generate Questions")
+    output_text = gr.Textbox(label="Generated Questions", lines=20)
+
+    generate_button.click(generate_questions, inputs=[context_input], outputs=[output_text])
+
+interface.launch()