Update app.py

app.py

@@ -1,19 +1,21 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import pytesseract
 
-# Load the tokenizer and model
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+# Initialize 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
-chat_model = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
 # Chat function
 def chat_fn(history, user_input):
     conversation = {"history": history, "user": user_input}
-    response = chat_model(user_input, max_length=50, num_return_sequences=1)
-    conversation["bot"] = response[0]['generated_text']
+    # Generate a response using the model
+    input_ids = tokenizer.encode(user_input, return_tensors="pt")
+    response = model.generate(input_ids=input_ids, max_length=50)
+    conversation["bot"] = tokenizer.decode(response[0], skip_special_tokens=True)
     history.append((user_input, conversation["bot"]))
     return history, ""