Update app.py

app.py

@@ -3,6 +3,11 @@ import subprocess
 import streamlit as st
 from huggingface_hub import snapshot_download, login
 
+if "quantized_model_path" not in st.session_state:
+    st.session_state.quantized_model_path = None
+if "upload_to_hf" not in st.session_state:
+    st.session_state.upload_to_hf = False
+
 def check_directory_path(directory_name: str) -> str:
     if os.path.exists(directory_name):
         path = os.path.abspath(directory_name)
@@ -31,7 +36,6 @@ def convert_to_gguf(model_dir, output_file):
     """
     st.write(f"🔄 Converting `{model_dir}` to GGUF format...")
     os.makedirs(os.path.dirname(output_file), exist_ok=True)
-    st.write(model_dir_path)
     cmd = [
         "python3", "/app/llama.cpp/convert_hf_to_gguf.py", model_dir,
         "--outtype", "f16", "--outfile", output_file
@@ -116,7 +120,6 @@ def upload_to_huggingface(file_path, repo_id, token):
     except Exception as e:
         st.error(f"❌ Failed to upload file: {e}")
 
-# Streamlit UI
 st.title("🦙 LLaMA Model Quantization (llama.cpp)")
 
 hf_model_name = st.text_input("Enter Hugging Face Model Name", "Qwen/Qwen2.5-1.5B")
@@ -125,20 +128,23 @@ start_button = st.button("🚀 Start Quantization")
 
 if start_button:
     with st.spinner("Processing..."):
-        quantized_model_path = automate_llama_quantization(hf_model_name, quant_type)
-    if quantized_model_path:
-        with open(quantized_model_path, "rb") as f:
-            st.download_button("⬇️ Download Quantized Model", f, file_name=os.path.basename(quantized_model_path))
-        upload_to_hf = st.checkbox("Upload to Hugging Face")
+        st.session_state.quantized_model_path = automate_llama_quantization(hf_model_name, quant_type)
+
+if st.session_state.quantized_model_path:
+    with open(st.session_state.quantized_model_path, "rb") as f:
+        st.download_button("⬇️ Download Quantized Model", f, file_name=os.path.basename(st.session_state.quantized_model_path))
+    
+    # Checkbox for upload section
+    st.session_state.upload_to_hf = st.checkbox("Upload to Hugging Face", value=st.session_state.upload_to_hf)
+    
+    if st.session_state.upload_to_hf:
+        st.write("### Upload to Hugging Face")
+        repo_id = st.text_input("Enter Hugging Face Repository ID (e.g., 'username/repo-name')")
+        hf_token = st.text_input("Enter Hugging Face Token", type="password")
         
-        if upload_to_hf:
-            st.write("### Upload to Hugging Face")
-            repo_id = st.text_input("Enter Hugging Face Repository ID (e.g., 'username/repo-name')")
-            hf_token = st.text_input("Enter Hugging Face Token", type="password")
-            
-            if st.button("📤 Upload to Hugging Face"):
-                if repo_id and hf_token:
-                    with st.spinner("Uploading..."):
-                        upload_to_huggingface(quantized_model_path, repo_id, hf_token)
-                else:
-                    st.warning("Please provide a valid repository ID and Hugging Face token.")
+        if st.button("📤 Upload to Hugging Face"):
+            if repo_id and hf_token:
+                with st.spinner("Uploading..."):
+                    upload_to_huggingface(st.session_state.quantized_model_path, repo_id, hf_token)
+            else:
+                st.warning("Please provide a valid repository ID and Hugging Face token.")