Update app.py

app.py

@@ -1,25 +1,19 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import pytesseract
 
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Arabic-Chat")
+model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Arabic-Chat")
 
 # Use a pipeline as a high-level helper
-from transformers import pipeline
-
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-pipe = pipeline("text-generation", model="sambanovasystems/SambaLingo-Arabic-Chat")
-pipe(messages)
-
-
+chat_model = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 # Chat function
 def chat_fn(history, user_input):
     conversation = {"history": history, "user": user_input}
-    # Use the model for Arabic
-    response = model.generate(input_ids=tokenizer.encode(user_input, return_tensors="pt"), max_length=50)
-    conversation["bot"] = tokenizer.decode(response[0], skip_special_tokens=True)
+    response = chat_model(user_input, max_length=50, num_return_sequences=1)
+    conversation["bot"] = response[0]['generated_text']
     history.append((user_input, conversation["bot"]))
     return history, ""