Updated app.py

app.py

@@ -1,25 +1,7 @@
-# Step 1: Clone the DeepSeek-VL2 repository (Only for Colab environments)
 import os
-if not os.path.exists("/content/DeepSeek-VL2"):
-    !git clone https://github.com/deepseek-ai/DeepSeek-VL2
-    %cd DeepSeek-VL2
-else:
-    print("Repository already cloned.")
-
-# Install dependencies if not already installed (you can add more if necessary)
-!pip install -r /content/DeepSeek-VL2/requirements.txt
-
-# Step 2: Ensure DeepSeek-VL2 is in the Python path
+import subprocess
 import sys
-sys.path.append('/content/DeepSeek-VL2')  # Add the DeepSeek-VL2 repository to the path
-
-# Step 3: Verify GPU (Optional)
 import torch
-print("CUDA Available:", torch.cuda.is_available())
-print("Device:", torch.cuda.current_device() if torch.cuda.is_available() else "CPU")
-print("Device Name:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "N/A")
-
-# Step 4: Rest of your app.py code
 import gradio as gr
 from PIL import Image
 from deepseek_vl2.serve.inference import load_model, deepseek_generate, convert_conversation_to_prompts
@@ -27,6 +9,23 @@ from deepseek_vl2.models.conversation import SeparatorStyle
 from deepseek_vl2.serve.app_modules.utils import configure_logger, strip_stop_words, pil_to_base64
 from google.colab import files
 
+# Step 1: Clone the DeepSeek-VL2 repository if not already present
+repo_dir = "/content/DeepSeek-VL2"
+if not os.path.exists(repo_dir):
+    subprocess.run(["git", "clone", "https://github.com/deepseek-ai/DeepSeek-VL2"], check=True)
+    sys.path.append(repo_dir)  # Add the DeepSeek-VL2 repository to the Python path
+else:
+    print("Repository already cloned.")
+
+# Step 2: Install dependencies if not already installed
+subprocess.run([sys.executable, "-m", "pip", "install", "-r", "/content/DeepSeek-VL2/requirements.txt"])
+
+# Step 3: Verify GPU (Optional)
+print("CUDA Available:", torch.cuda.is_available())
+print("Device:", torch.cuda.current_device() if torch.cuda.is_available() else "CPU")
+print("Device Name:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "N/A")
+
+# Step 4: Define your model and prediction functions
 logger = configure_logger()
 
 MODELS = ["deepseek-ai/deepseek-vl2-tiny"]
@@ -139,3 +138,4 @@ with gr.Blocks() as demo:
     submit_btn.click(upload_and_process, inputs=image_input, outputs=[output_text, history_state])
 
 demo.launch(share=True, debug=True)  # Added debug=True for more Gradio logs
+