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import gradio as gr
import os
import pdfplumber
import tempfile
from huggingface_hub import InferenceClient
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
import os
from huggingface_hub import InferenceClient
hf_tokens = os.environ.get("hf_token")
# client = InferenceClient(
# provider="novita",
# api_key=hf_tokens
# )
# Initialize Hugging Face InferenceClient
client = InferenceClient(
provider="novita",
api_key=hf_tokens #"hf_xxxxxxxxxxxxxxxxxxxxxxxxx" # Replace with your HF token
)
# Global vectorstore
vectorstore = None
# Load and process the uploaded PDF
def load_pdf(file):
global vectorstore
try:
# Save uploaded file to temp path (file is already bytes in Kaggle!)
temp_pdf_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name
with open(temp_pdf_path, "wb") as f:
f.write(file) # <--- FIXED LINE
# Extract text using pdfplumber
import pdfplumber
raw_text = ""
with pdfplumber.open(temp_pdf_path) as pdf:
for page in pdf.pages:
text = page.extract_text()
if text:
raw_text += text + "\n"
if not raw_text.strip():
return "❌ No extractable text found in the PDF."
# Chunk the text
splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
texts = splitter.split_text(raw_text)
# Create FAISS vectorstore
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.from_texts(texts, embeddings)
return "✅ PDF successfully processed. You can now ask questions!"
except Exception as e:
return f"❌ Error: {str(e)}"
def ask_question(query):
global vectorstore
if vectorstore is None:
return "❌ Please upload a PDF first."
try:
docs = vectorstore.similarity_search(query, k=3)
context = "\n\n".join([doc.page_content for doc in docs])
# Prepare chat message format
messages = [
{
"role": "system",
"content": "You are a helpful assistant that answers questions based on a document."
},
{
"role": "user",
"content": f"Answer this question using the context below:\n\nContext:\n{context}\n\nQuestion:\n{query}"
}
]
# Use chat.completions.create
completion = client.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=messages,
max_tokens=500
)
return completion.choices[0].message.content.strip()
except Exception as e:
return f"❌ Failed to generate answer: {str(e)}"
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 📄 RAG PDF Chatbot using Hugging Face Inference API")
with gr.Row():
file_input = gr.File(label="Upload PDF", type="binary")
upload_btn = gr.Button("Process")
status_box = gr.Textbox(label="Status", interactive=False)
with gr.Row():
question = gr.Textbox(label="Ask a Question")
ask_btn = gr.Button("Ask")
answer = gr.Textbox(label="Answer", lines=6)
upload_btn.click(load_pdf, inputs=file_input, outputs=status_box)
ask_btn.click(ask_question, inputs=question, outputs=answer)
demo.launch()
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