Update app.py

app.py

@@ -1,26 +1,17 @@
 import gradio as gr
-from transformers import pipeline
-from PIL import Image
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import pytesseract
 
+# Login to huggingface CLI
 huggingface-cli login
 
-# Initialize chat model
-chat_model = pipeline("text-generation", model="gpt2")  # عدّل اسم النموذج حسب الحاجة
-
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-pipe = pipeline("text-generation", model="meta-llama/Llama-3.3-70B-Instruct")
-pipe(messages)
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
+# Initialize chat model (You can change the model here)
+chat_model = pipeline("text-generation", model="gpt2")  # You can switch to any model of your choice
 
+# Initialize LLaMA model for more advanced instruction-following tasks
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")
 model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")
+
 # Chat function
 def chat_fn(history, user_input):
     conversation = {"history": history, "user": user_input}
@@ -54,4 +45,5 @@ with gr.Blocks() as demo:
         msg.submit(chat_fn, [chatbot, msg], [chatbot, msg])
         clear.click(lambda: None, None, chatbot)
 
+# Launch the Gradio interface
 demo.launch()