app.pyc

🧠 Quest AI Assistant – RAG + Voice PDF Q&A 🤖🎤

requirements_new.txt → app.pyc

@@ -12,14 +12,13 @@ from langchain.text_splitter import CharacterTextSplitter
 from sentence_transformers import SentenceTransformer
 from langchain.vectorstores import FAISS
 from langchain.chains import RetrievalQA
-from langchain.llms import HuggingFacePipeline
 from transformers import pipeline
 from gtts import gTTS
 from io import BytesIO
 
 # -------- PDF Reading --------
-def read_pdf(file):
-    pdf_reader = PyPDF2.PdfReader(file)
+def read_pdf(file_path):
+    pdf_reader = PyPDF2.PdfReader(file_path)
     text = ""
     for page in pdf_reader.pages:
         text += page.extract_text()
@@ -47,7 +46,7 @@ def answer_question(vectorstore, query):
     answer = text_gen(context + "\nQ: " + query + "\nA:", max_length=150)[0]["generated_text"]
     return answer
 
-# -------- Voice --------
+# -------- Text-to-Speech --------
 def text_to_speech(text):
     tts = gTTS(text)
     audio_fp = BytesIO()
@@ -66,12 +65,10 @@ def process(pdf_file, question):
 
 interface = gr.Interface(
     fn=process,
-    inputs=[gr.File(label="Upload PDF"), gr.Textbox(label="Ask Question")],
+    inputs=[gr.File(label="Upload PDF"), gr.Textbox(label="Ask a Question")],
     outputs=[gr.Textbox(label="Answer"), gr.Audio(label="Answer in Voice")],
     title="🧠 Quest AI Assistant – RAG + Voice PDF Q&A 🤖🎤",
-    description="Upload a PDF, ask a question, and get the answer in text and voice!"
+    description="Upload any PDF and ask questions. Get answers in text and voice instantly!"
 )
 
 interface.launch()
-
-