Update app.py

app.py

@@ -23,25 +23,25 @@ def respond(encoded_smiles, max_tokens, temperature, top_p, top_k):
     try:
         # Load the Llama model
         model_name = "model.gguf"
-        llm = Llama(model_name)  # Khởi tạo đối tượng Llama với tệp mô hình
+        llm = Llama(model_name)  # Khởi tạo đối tượng Llama với tên mô hình
         
+        # Tokenize the input
+        input_ids = llm.tokenize(encoded_smiles)  # Mã hóa đầu vào thành các IDs token
+
         # Set generation settings
         settings = {
-            "max_new_tokens": max_tokens,
-            "temperature": temperature,
-            "top_p": top_p,
-            "top_k": top_k,
+            "max_new_tokens": int(max_tokens),
+            "temperature": float(temperature),
+            "top_p": float(top_p),
+            "top_k": int(top_k),
             "do_sample": True,
         }
 
-        # Tokenize the input
-        input_ids = llm.tokenize(encoded_smiles)
-
         # Generate the output
-        outputs = llm.generate(input_ids=input_ids, **settings)
+        outputs = llm.generate(input_ids, **settings)
 
         # Decode the output tokens to text
-        output_text = llm.decode(outputs[0], skip_special_tokens=True)
+        output_text = llm.decode(outputs[0])
 
         # Extract the predicted selfies from the output text
         first_inst_index = output_text.find("[/INST]")
@@ -56,10 +56,10 @@ demo = gr.Interface(
     fn=respond,
     inputs=[
         gr.Textbox(label="Encoded SMILES"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=1.0, step=0.05, label="Top-p"),
-        gr.Slider(minimum=0, maximum=100, value=50, step=1, label="Top-k")
+        gr.Slider(minimum=1, maximum=2048, default=512, label="Max tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, default=1.0, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, default=1.0, label="Top-p"),
+        gr.Slider(minimum=0, maximum=100, default=50, label="Top-k")
     ],
     outputs=gr.JSON(label="Results"),
     theme=gr.themes.Soft(primary_hue="violet", secondary_hue="violet", neutral_hue="gray", font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(