added logs along the way - debugging

app.py

@@ -1,19 +1,45 @@
 import gradio as gr
-#from transformers import pipeline
-#from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
+# Load the tokenizer and model
+print("Loading tokenizer...")
 tokenizer = AutoTokenizer.from_pretrained("kolbeins/model")
-model = AutoModelForCausalLM.from_pretrained("kolbeins/model")
+print("Tokenizer loaded.")
 
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained("kolbeins/model")
+print("Model loaded.")
 
 def chat(input_txt):
-    inputs = tokenizer(input_txt, return_tensors="pt")
-    outputs = model.generate(**inputs)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+    """
+    Function to generate a response using the model for the given input text.
+    """
+    try:
+        print("Tokenizing input...")
+        # Tokenizing the input text, making sure to add special tokens if necessary
+        inputs = tokenizer(input_txt, return_tensors="pt", padding=True, truncation=True, max_length=512)
+        print(f"Tokenized inputs: {inputs}")
 
+        print("Generating output...")
+        # Generate the output using the model
+        outputs = model.generate(**inputs)
+        print(f"Generated output: {outputs}")
 
+        print("Decoding output...")
+        # Decode the output (the model generates token IDs, so we need to decode them back to text)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        print(f"Decoded response: {response}")
+        
+        # Return the generated response
+        return response
 
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        return f"Error: {e}"
+
+# Define the Gradio interface for the chatbot
 demo = gr.Interface(fn=chat, inputs="text", outputs="text")
-demo.launch()
+
+# Launch the interface
+print("Launching Gradio interface...")
+demo.launch()