Update app.py

app.py

@@ -1,11 +1,14 @@
 import gradio as gr
 import os
+import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from langchain_community.vectorstores import Chroma
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import TextLoader
-from langchain_community.embeddings import HuggingFaceEmbeddings
-import torch
+from langchain_huggingface import HuggingFaceEmbeddings  # ✅ UPDATED IMPORT
+
+# Fix Torch SDPA issue
+os.environ["TORCH_USE_CUDA_DSA"] = "1"
 
 # Load Hugging Face model & tokenizer
 MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
@@ -49,9 +52,9 @@ def find_related_documents(query):
     return vector_db.similarity_search(query)
 
 # Function to generate answers using Hugging Face Model
-def generate_answer(question, uploaded_file):
+def generate_answer(question, uploaded_file=None):
     # Process file if uploaded
-    if uploaded_file:
+    if uploaded_file is not None:
         file_path = os.path.join(UPLOAD_FOLDER, uploaded_file.name)
         with open(file_path, "wb") as f:
             f.write(uploaded_file.read())
@@ -71,13 +74,20 @@ def generate_answer(question, uploaded_file):
 
     return answer
 
-# Gradio UI for chatbot
+# Gradio UI for chatbot with File Upload Support ✅ FIXED
+def chatbot_interface(question, uploaded_file):
+    return generate_answer(question, uploaded_file)
+
 with gr.Blocks() as demo:
     gr.Markdown("### 📘 DocuMind AI - DeepSeek Qwen Chatbot")
     
-    chatbot = gr.ChatInterface(generate_answer)
-    file_input = gr.File(label="Upload a Document (Optional)")
-    
-    chatbot.chatbot.input_components.append(file_input)
+    with gr.Row():
+        question_input = gr.Textbox(label="Ask a Question")
+        file_input = gr.File(label="Upload a Document (Optional)")
+
+    submit_btn = gr.Button("Get Answer")
+    output_text = gr.Textbox(label="Answer")
+
+    submit_btn.click(chatbot_interface, inputs=[question_input, file_input], outputs=output_text)
 
 demo.launch()