Update app.py

app.py

@@ -2,30 +2,45 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 import spaces
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # Load the model and tokenizer
 model_name = "mrcuddle/SD-Prompter"
+logging.info(f"Loading model and tokenizer for {model_name}")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
+logging.info("Model and tokenizer loaded successfully")
 
 @spaces.GPU
 # Function to generate a response
 def chat(message, history):
+    logging.info(f"Received message: {message}")
+    logging.info(f"Chat history: {history}")
+
     # Combine the message and history into a single input
     input_text = " ".join([f"{user}: {msg}" for user, msg in history] + [f"User: {message}"])
+    logging.info(f"Input text: {input_text}")
+
     inputs = tokenizer(input_text, return_tensors="pt")
+    logging.info(f"Tokenized input: {inputs}")
 
     # Generate a response
     with torch.no_grad():
         outputs = model.generate(inputs.input_ids, max_length=300, num_return_sequences=1)
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    logging.info(f"Generated response: {response}")
 
     # Extract only the new response part
     response = response.replace(input_text, "").strip()
+    logging.info(f"Extracted response: {response}")
 
     # Append the new message and response to the history
     history.append(("User", message))
     history.append(("Assistant", response))
+    logging.info(f"Updated chat history: {history}")
 
     return history, history
 
@@ -37,4 +52,5 @@ iface = gr.ChatInterface(
 )
 
 # Launch the interface
+logging.info("Launching Gradio interface")
 iface.launch()