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app.py
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app.py
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APP.PY
Verified signature🧠 Quest AI Assistant – RAG + Voice PDF Q&A 🤖📄🎤a9e7dfe -
Added app.py and requirements.txt
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app.pyc
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requirements_new.txt
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requirements_new.txt
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requirements.txtapp.py
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app.py
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requirements.txt
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Rename app.py to requirements.txt
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app.py
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app.py
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app.py
Verified signatureimport PyPDF2 from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import SentenceTransformerEmbeddings from langchain.vectorstores import FAISS from langchain.chains import RetrievalQA from langchain.llms import HuggingFaceHub import gradio as gr from gtts import gTTS import os # --- PDF Text Extraction --- def extract_text(pdf_file): text = "" reader = PyPDF2.PdfReader(pdf_file) for page in reader.pages: text += page.extract_text() return text # --- Text Splitting --- def split_text(text): splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) return splitter.split_text(text) # --- Create Vector Store --- def create_vectorstore(texts): embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") vectorstore = FAISS.from_texts(texts, embeddings) return vectorstore # --- Setup RAG (retrieval + LLM) --- def setup_rag(pdf_file): text = extrac…d9f8e93 -
app.py
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app.py
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Update app.py
Verified signatureimport gradio as gr from PyPDF2 import PdfReader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import SentenceTransformerEmbeddings from langchain.vectorstores import FAISS from langchain.chains import RetrievalQA from langchain.llms import HuggingFaceHub import datetime # 🔹 Hugging Face API token set karo import os os.environ["HUGGINGFACEHUB_API_TOKEN"] = "YOUR_HF_TOKEN_HERE" # <-- Replace with your token # 🔹 Embeddings setup embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") # 🔹 PDF text extraction function def extract_text(file): pdf = PdfReader(file.name) text = "" for page in pdf.pages: page_text = page.extract_text() if page_text: text += page_text return text # 🔹 QA system def quest_ai_assistant(pdf_file, user_query): start_time = datetime.datetime.now() # PDF se text extract karo text = extract_text(pdf_file) # Text split karo splitter …a9a3b91 -
app.py
Verified signatureimport gradio as gr from transformers import pipeline import datetime import pyttsx3 # 🔹 Model load karo (fast + accurate) model = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2") # 🔹 Voice output setup engine = pyttsx3.init() engine.setProperty('rate', 170) def quest_ai_assistant(user_input, voice_output): # 🕒 Start time for processing start_time = datetime.datetime.now() # 🔹 Generate AI response response = model(user_input, max_new_tokens=150, do_sample=True, temperature=0.7)[0]["generated_text"] # 🕒 End time and duration end_time = datetime.datetime.now() duration = (end_time - start_time).seconds # 🔊 Voice output option if voice_output: engine.say(response) engine.runAndWait() return f"🧠 Quest AI: {response}\n\n⚡ Processing Time: {duration} sec" # 🎨 Gradio UI (side-by-side layout) with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown("## 🤖 Quest AI Assistant\nAsk anything, get …1d511e5 -
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