Update app.py

app.py

@@ -1,64 +1,130 @@
+# Import libraries
+import ollama
+from PyPDF2 import PdfReader
+import tiktoken
+import groq
+import faiss
+import numpy as np
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import json
+import os
+import pickle
+
+# == Buat folder models ==
+os.makedirs("models", exist_ok=True)
+
+# == Load API Key dari File (Hindari Hardcoded Key) ==
+def load_api_key():
+    with open("config.json", "r") as f:
+        config = json.load(f)
+    return config["GROQ_API_KEY"]
+
+GROQ_API_KEY = load_api_key()
+
+# == Ekstraksi Teks dari PDF ==
+def extract_text_from_pdf(pdf_file: str) -> str:
+    """Ekstrak teks dari PDF dan gabungkan menjadi satu string."""
+    with open(pdf_file, 'rb') as pdf:
+        reader = PdfReader(pdf)
+        text = " ".join(page.extract_text() or "" for page in reader.pages)
+    return text
+
+# == Chunking Teks ==
+def chunk_text(text: str, max_tokens: int = 512) -> list:
+    """Membagi teks menjadi chunk berdasarkan token menggunakan tokenizer OpenAI."""
+    tokenizer = tiktoken.get_encoding("cl100k_base")  # Gunakan tokenizer OpenAI
+    tokens = tokenizer.encode(text)
+
+    chunks = []
+    for i in range(0, len(tokens), max_tokens):
+        chunk_tokens = tokens[i:i+max_tokens]
+        chunk_text = tokenizer.decode(chunk_tokens)
+        chunks.append(chunk_text)
+
+    return chunks
+
+# == Embedding dengan Ollama ==
+def get_embedding(text: str):
+    """Mendapatkan embedding dari teks menggunakan Ollama."""
+    embedding = ollama.embed(model="mxbai-embed-large", input=text)
+    return np.array(embedding["embeddings"][0], dtype=np.float32)  # Pastikan mengambil list pertama
+
+# == Simpan Embedding ke FAISS ==
+d = 1024  # Dimensi embedding dari model `mxbai-embed-large`
+index = faiss.IndexFlatL2(d)  # Inisialisasi FAISS Index
+text_chunks = []
+
+def add_to_db(text_chunks_local):
+    """Menambahkan embedding ke FAISS."""
+    global text_chunks
+    text_chunks = text_chunks_local  # Simpan chunk ke global var
+    embeddings = np.array([get_embedding(text) for text in text_chunks], dtype=np.float32)
+    index.add(embeddings)
+
+def search_db(query, k=5):
+    """Melakukan pencarian query dalam FAISS Index."""
+    query_embedding = np.array([get_embedding(query)], dtype=np.float32).reshape(1, -1)
+    distances, indices = index.search(query_embedding, k)
+    return [text_chunks[i] for i in indices[0]]  # Ambil teks chunk yang relevan
+
+def save_to_faiss(index_path="vector_index.faiss"):
+    """Menyimpan FAISS index ke file."""
+    faiss.write_index(index, index_path)
+
+def load_faiss(index_path="vector_index.faiss"):
+    """Memuat kembali FAISS index dari file."""
+    global index
+    index = faiss.read_index(index_path)
+
+# == Simpan dan Load Model Embedding ==
+def save_embeddings(embeddings_path="models/embeddings.pkl"):
+    with open(embeddings_path, "wb") as f:
+        pickle.dump(index, f)
+
+def load_embeddings(embeddings_path="models/embeddings.pkl"):
+    global index
+    with open(embeddings_path, "rb") as f:
+        index = pickle.load(f)
+
+# == Integrasi LLaMA via Groq API ==
+client = groq.Client(api_key=GROQ_API_KEY)
+
+def query_llama(prompt):
+    """Menggunakan LLaMA untuk menjawab pertanyaan dengan konteks yang diberikan."""
+    response = client.chat.completions.create(
+        model="llama3-8b-8192",
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=512
+    )
+    return response.choices[0].message.content.strip()
+
+# == Main Workflow ==
+if __name__ == '__main__':
+    pdf_text = extract_text_from_pdf('dini_anggriyani_synthetic_data.pdf')
+    text_chunks = chunk_text(pdf_text, max_tokens=1024)  # Sesuaikan dengan LLaMA
+
+    # Tambahkan ke database FAISS
+    add_to_db(text_chunks)
+    save_to_faiss()  # Simpan FAISS index
+    save_embeddings()  
+
+    # Tes pencarian RAG
+    retrieved_chunks = search_db("Apa isi dokumen ini?")
+    context = "\n".join(retrieved_chunks)
+    
+    prompt = f"Gunakan informasi berikut untuk menjawab:\n{context}\n\nPertanyaan: Apa isi dokumen ini?"
+    answer = query_llama(prompt)
+    print(answer)
+
+# == Buat Chatbot Interface ==
+def chatbot_interface(user_query):
+    retrieved_chunks = search_db(user_query)  # Sudah berupa teks
+    context = "\n".join(retrieved_chunks)
+    
+    prompt = f"Gunakan informasi berikut untuk menjawab:\n{context}\n\nPertanyaan: {user_query}"
+    answer = query_llama(prompt)
+    
+    return answer
+
+iface = gr.Interface(fn=chatbot_interface, inputs="text", outputs="text", title="RAG Chatbot")
+iface.launch()