Update app.py

app.py

@@ -2,16 +2,17 @@ from langchain_community.document_loaders import PyPDFLoader
 import gradio as gr
 from langchain.chains.summarize import load_summarize_chain
 from huggingface_hub import InferenceClient
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-pipe = pipeline("summarization", model="facebook/bart-large-cnn")
-
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from langchain.llms import HuggingFaceHub
+import os
+# Set your Hugging Face token securely
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = ""
+
+# Create the LLM
+llm = HuggingFaceHub(
+    repo_id="facebook/bart-large-cnn",  # Summarization-capable model
+    model_kwargs={"temperature": 0.7, "max_length": 512}
+)
 
-tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
-model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
 
 #TEXT_MODEL_NAME = "google/gemma-3-270m"
 loader = PyPDFLoader("http://arxiv.org/pdf/2508.13246v1") 
@@ -21,7 +22,7 @@ documents = loader.load()
 def summarize_pdf (pdf_file_path, custom_prompt=""):
     loader = PyPDFLoader(pdf_file_path)
     docs = loader.load_and_split()
-    chain = load_summarize_chain(model, chain_type="map_reduce")
+    chain = load_summarize_chain(llm, chain_type="map_reduce")
     summary = chain.run(docs)
     
     return summary