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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from typing import List, Tuple | |
| import fitz # PyMuPDF | |
| from sentence_transformers import SentenceTransformer, util | |
| import numpy as np | |
| import faiss | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Placeholder for the app's state | |
| class MyApp: | |
| def __init__(self) -> None: | |
| self.documents = [] | |
| self.embeddings = None | |
| self.index = None | |
| self.load_pdf("Mypdf.pdf") # Replace with the actual PDF filename | |
| self.build_vector_db() | |
| def load_pdf(self, file_path: str) -> None: | |
| """Extracts text from a PDF file and stores it in the app's documents.""" | |
| doc = fitz.open(file_path) | |
| self.documents = [] | |
| for page_num in range(len(doc)): | |
| page = doc[page_num] | |
| text = page.get_text() | |
| self.documents.append({"page": page_num + 1, "content": text}) | |
| print("PDF processed successfully!") | |
| def build_vector_db(self) -> None: | |
| """Builds a vector database using the content of the PDF.""" | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| self.embeddings = model.encode([doc["content"] for doc in self.documents]) | |
| self.index = faiss.IndexFlatL2(self.embeddings.shape[1]) | |
| self.index.add(np.array(self.embeddings)) | |
| print("Vector database built successfully!") | |
| def search_documents(self, query: str, k: int = 3) -> List[str]: | |
| """Searches for relevant documents using vector similarity.""" | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| query_embedding = model.encode([query]) | |
| D, I = self.index.search(np.array(query_embedding), k) | |
| results = [self.documents[i]["content"] for i in I[0]] | |
| return results if results else ["No relevant documents found."] | |
| app = MyApp() | |
| def respond( | |
| message: str, | |
| history: List[Tuple[str, str]], | |
| system_message: str, | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float, | |
| ): | |
| system_message = "You are a good presenter and advice Study material based on Mypdf guide to Focus on Goals.You provide helpful advice on managing stress. You ask one question at a time when clarification is needed. Remember to be polite and considerate in your responses. Use the guide to provide accurate and helpful information for focusing on your goals." | |
| 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}) | |
| # RAG - Retrieve relevant documents | |
| retrieved_docs = app.search_documents(message) | |
| context = "\n".join(retrieved_docs) | |
| messages.append({"role": "system", "content": "Relevant information: " + context}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=100, | |
| stream=True, | |
| temperature=0.7, | |
| top_p=0.9, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown( | |
| "‼️Disclaimer: This chatbot is based on Mypdf guide and is intended for general advice on managing stress and study schedule.‼️" | |
| ) | |
| chatbot = gr.ChatInterface( | |
| respond, | |
| examples=[ | |
| ["How I feel positive about my studies?"], | |
| ["What do meditation relax my stress?"], | |
| ["Can you guide me about my career Options?"], | |
| ["How do I ignore the negative facts and remain positive?"], | |
| ], | |
| title='Focus on your goals❤️🕊️' | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |