Update app.py

app.py

@@ -1,7 +1,13 @@
-import gradio as gr
+ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import torch
 import os
+import gradio as gr
+import transformers
+
+print(f"Gradio version: {gr.__version__}") # Print Gradio version
+print(f"Transformers version: {transformers.__version__}") # Print Transformers version
+
 
 # --- Configuration (Read from Environment Variables) ---
 
@@ -30,6 +36,8 @@ try:
         tokenizer.pad_token = tokenizer.eos_token
     tokenizer.padding_side = "right"
 
+    print("Model and tokenizer loaded successfully!") # Success message
+
 except OSError as e:
     print(f"Error loading model or tokenizer: {e}")
     print("Ensure MODEL_PATH and TOKENIZER_PATH environment variables are set correctly.")
@@ -42,7 +50,18 @@ def chat_with_llm(prompt, history):
     """Generates a response from the LLM, handling history correctly."""
     formatted_prompt = ""
     if history:
+        print("DEBUG: History variable type:", type(history))
+        if history:
+            print("DEBUG: Example history item:", history[0]) # Print first history item
+        else:
+            print("DEBUG: History is empty but should not be in chat turn > 1")
+
+
         for item in history:
+            if not isinstance(item, dict) or "role" not in item or "content" not in item: # Check item structure
+                print("DEBUG: Invalid history item format:", item) # Debug invalid item
+                continue # Skip invalid items instead of crashing
+
             if item["role"] == "user":
                 formatted_prompt += f"{tokenizer.bos_token}{item['content']}{tokenizer.eos_token}"
             elif item["role"] == "assistant":
@@ -72,19 +91,21 @@ def chat_with_llm(prompt, history):
 # --- Gradio Interface ---
 # Use the 'messages' format for chatbot
 def predict(message, history):
-    history = history or []
-    response = chat_with_llm(message, history)
-    history.append({"role": "user", "content": message})
-    history.append({"role": "assistant", "content": response})
-    return "", history
+    history_messages = history or [] # Rename to avoid shadowing
+    response = chat_with_llm(message, history_messages)
+    history_messages.append({"role": "user", "content": message})
+    history_messages.append({"role": "assistant", "content": response})
+    return "", history_messages  # Return the updated history
+
 
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot(label="Athspi Chat", height=500, show_label=True,
                          value=[{"role": "assistant", "content": "Hi! I'm Athspi. How can I help you today?"}],
-                         type="messages")  # Set type to "messages"
+                         type="messages")  # Ensure type is "messages"
     msg = gr.Textbox(label="Your Message", placeholder="Type your message here...")
     clear = gr.Button("Clear")
 
+
     msg.submit(predict, [msg, chatbot], [msg, chatbot])
     clear.click(lambda: [], [], chatbot, queue=False)