Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+import faiss
+import numpy as np
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+def answer_from_pdf(pdf_file, user_question):
+    # ——————————————————————
+    # 1) PDF Text Extraction
+    # ——————————————————————
+
+    pdf_reader = PyPDF2.PdfReader(pdf_file.name)
+    raw_text = ""
+    for page in pdf_reader.pages:
+        raw_text += page.extract_text()
+
+    # ——————————————————————
+    # 2) Chunking with LangChain
+    # ——————————————————————
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size    = 1000,
+        chunk_overlap = 200,
+        separators    = ["\n\n", "\n", " ", ""]
+    )
+    chunks = splitter.split_text(raw_text)
+
+    # ——————————————————————————
+    # 3) Embedding & FAISS Indexing
+    # ——————————————————————————
+    hf_embedder = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+    )
+    doc_embeddings = hf_embedder.embed_documents(chunks)
+    emb_array = np.array(doc_embeddings, dtype=np.float32)
+    index = faiss.IndexFlatL2(emb_array.shape[1])
+    index.add(emb_array)
+
+    # ——————————————————————————
+    # 4) Load LLAma model
+    # ——————————————————————————
+    gguf_path = hf_hub_download(
+        repo_id="bartowski/Meta-Llama-3.1-8B-Instruct-GGUF",
+        filename="Meta-Llama-3.1-8B-Instruct-Q6_K.gguf",
+    )
+    llama = Llama(model_path=gguf_path, n_ctx=2048)
+
+    # ——————————————————————————
+    # 5) Retrieval & Prompting
+    # ——————————————————————————
+    system_msg = (
+        "You are a knowledgeable assistant. "
+        "Use the provided context to answer the user's question concisely, "
+        "if it's not in the provided context, you say I don't know"
+        "avoid hallucinations, and keep answers under 150 words."
+    )
+    q_emb = hf_embedder.embed_query(user_question)
+    q_vec = np.array(q_emb, dtype=np.float32).reshape(1, -1)
+    D, I = index.search(q_vec, k=3)
+    context_chunks = [chunks[i] for i in I[0]]
+    context_text = "\n\n".join(context_chunks)
+
+    prompt = (
+        f"SYSTEM:\n{system_msg}\n\n"
+        f"CONTEXT:\n{context_text}\n\n"
+        f"USER:\n{user_question}\n\n"
+        "ASSISTANT:"
+    )
+
+    resp = llama.create_completion(
+        prompt=prompt,
+        max_tokens=256,
+        temperature=0.7,
+        stop=["\n\n"]
+    )
+    return resp["choices"][0]["text"].strip()
+
+# Build a chat interface
+with gr.Blocks() as demo:
+    gr.Markdown("## PDF RAG Chatbot (LangChain + LLaMA)")
+    upload = gr.File(label="Upload PDF")
+    chatbot = gr.Chatbot()
+    user_input = gr.Textbox(placeholder="Type your question and hit Send")
+    send_btn = gr.Button("Send")
+
+    def chat_step(pdf_file, message, chat_history):
+        response = answer_from_pdf(pdf_file, message)
+        chat_history = chat_history + [(message, response)]
+        return chat_history, ""
+
+    send_btn.click(
+        chat_step,
+        inputs=[upload, user_input, chatbot],
+        outputs=[chatbot, user_input]
+    )
+
+if __name__ == "__main__":
+    demo.launch()