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9675ff7 bf17205 9675ff7 e182395 9675ff7 d081b1b 9675ff7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import os
# os.environ["GROQ_API_KEY"] = "YOUR_GROQ_API_KEY"
# from google.colab import userdata
# GROQ_API_KEY=userdata.get('rag-based')
import gradio as gr
from groq import Groq
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
# -----------------------------
# Environment Setup
# -----------------------------
# GROQ_API_KEY = os.environ.get("Rag-based")
client = Groq(api_key=os.environ.get("Rag_based"))
# -----------------------------
# Global Variables
# -----------------------------
vector_db = None
# -----------------------------
# Embedding Model
# -----------------------------
embedding_model = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
# -----------------------------
# Document Processing Function
# -----------------------------
def process_document(pdf_file):
global vector_db
if pdf_file is None:
return "Please upload a PDF Document first."
try:
# Load PDF
loader = PyPDFLoader(pdf_file.name)
documents = loader.load()
# Chunking
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)
# Create FAISS vector database
vector_db = FAISS.from_documents(
chunks,
embedding_model
)
return f"Document processed successfully. {len(chunks)} chunks of your document created. Now, proceed to ask your question ahead."
except Exception as e:
return f"Error processing document: {str(e)}"
# -----------------------------
# Question Answering Function
# -----------------------------
def ask_question(question):
global vector_db
if vector_db is None:
return "Please upload and process a PDF document first."
try:
# Retrieve relevant chunks
docs = vector_db.similarity_search(question, k=4)
context = "\n\n".join([doc.page_content for doc in docs])
prompt = f"""
You are a helpful assistant. Answer the question ONLY using the following context.
If the answer is not in the context, say "I could not find the answer in the provided context."
Context:
{context}
Question:
{question}
Answer clearly and based only on the provided context.
"""
# Groq LLM call
chat_completion = client.chat.completions.create(
messages=[
{"role": "user", "content": prompt}
],
model="llama-3.3-70b-versatile",
)
response = chat_completion.choices[0].message.content
return response
except Exception as e:
return f"Error generating answer: {str(e)}"
# -----------------------------
# Gradio Interface
# -----------------------------
with gr.Blocks() as demo:
gr.Markdown("# 📄 PDF Document Assistant Developed by Asif Jamal")
gr.Markdown(
"Upload a PDF document and ask questions about its content."
)
pdf_input = gr.File(label="Upload PDF Document")
process_button = gr.Button("Click to Process Document")
process_output = gr.Textbox(label="Processing Status")
process_button.click(
process_document,
inputs=pdf_input,
outputs=process_output
)
gr.Markdown("## Ask Questions")
question_input = gr.Textbox(
label="Enter your question."
)
ask_button = gr.Button("Click to Proceed")
answer_output = gr.Textbox(
label="Answer",
lines=10
)
ask_button.click(
ask_question,
inputs=question_input,
outputs=answer_output
)
gr.Markdown(
"""
---
© 2026 AI Document Assistant
Developed by Asif Jamal
"""
)
demo.launch() |