Update app.py

app.py

@@ -1,27 +1,23 @@
 import os
 import tempfile
-import torch
 import gradio as gr
 from langchain_community.vectorstores import FAISS
 from langchain_groq import ChatGroq
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings  # Fixed import
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_core.runnables import RunnablePassthrough
-from langchain_community.document_loaders import PyPDFLoader  # Fixed import
+from langchain.document_loaders import PyPDFLoader
 from langchain import hub
 
 # Set API key (Replace with your actual key)
-os.environ["GROQ_API_KEY"] = "your_groq_api_key"
+os.environ["GROQ_API_KEY"] = "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"
 
-# Check if GPU is available
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Initialize LLM and Embeddings with GPU if available
+# Initialize LLM and Embeddings
 llm = ChatGroq(model="llama3-8b-8192")
 model_name = "BAAI/bge-small-en"
-hf_embeddings = HuggingFaceBgeEmbeddings(  # Fixed import
+hf_embeddings = HuggingFaceBgeEmbeddings(
     model_name=model_name,
-    model_kwargs={'device': device},
+    model_kwargs={'device': 'cpu'},
     encode_kwargs={'normalize_embeddings': True}
 )
 
@@ -68,28 +64,23 @@ def ask_question(query):
     if "rag_chain" not in globals():
         return "Please upload and process a PDF first."
     
-    response = rag_chain.invoke(query)
+    response = rag_chain.invoke(query).content
     return response
 
-# Gradio UI with Mobile-Friendly Fixes
+# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# 📄 PDF Chatbot with RAG")
     gr.Markdown("Upload a PDF and ask questions!")
-
-    with gr.Row():
-        pdf_input = gr.File(label="Upload PDF", type="binary")
-        process_button = gr.Button("Process PDF")
-
+    
+    pdf_input = gr.File(label="Upload PDF", type="binary")
+    process_button = gr.Button("Process PDF")
     output_message = gr.Textbox(label="Status", interactive=False)
-
-    with gr.Row():
-        query_input = gr.Textbox(label="Ask a Question")
-        submit_button = gr.Button("Submit")
-
+    
+    query_input = gr.Textbox(label="Ask a Question")
+    submit_button = gr.Button("Submit")
     response_output = gr.Textbox(label="AI Response")
 
     process_button.click(process_pdf, inputs=pdf_input, outputs=output_message)
     submit_button.click(ask_question, inputs=query_input, outputs=response_output)
 
-# 🚀 Fixed launch (removed invalid 'theme' argument)
-demo.launch(share=True)
+demo.launch()