Update app.py

app.py

@@ -2,67 +2,86 @@ import gradio as gr
 import torch
 from transformers import AutoModelForSeq2SeqLM, T5Tokenizer
 import time
+import sys
+import traceback
+
+# Global variables to store error information
+error_message = ""
 
 # Load the model and tokenizer from Hugging Face
 model_name = "ambrosfitz/history-qa-t5-base"
 try:
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
     tokenizer = T5Tokenizer.from_pretrained(model_name, use_fast=False)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
 except Exception as e:
-    print(f"Error loading model or tokenizer: {e}")
-    raise
-
-# Set device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
+    error_message = f"Error loading model or tokenizer: {str(e)}\n{traceback.format_exc()}"
+    print(error_message)
 
 def generate_qa(text, max_length=512):
-    input_text = f"Generate question: {text}"
-    input_ids = tokenizer(input_text, return_tensors="pt", max_length=max_length, truncation=True).input_ids.to(device)
-    
-    with torch.no_grad():
-        outputs = model.generate(input_ids, max_length=max_length, num_return_sequences=1, do_sample=True, temperature=0.7)
-    
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    # Parse the generated text
-    parts = generated_text.split("Question: ")
-    if len(parts) > 1:
-        qa_parts = parts[1].split("Options:")
-        question = qa_parts[0].strip()
+    try:
+        input_text = f"Generate question: {text}"
+        input_ids = tokenizer(input_text, return_tensors="pt", max_length=max_length, truncation=True).input_ids.to(device)
         
-        options_and_answer = qa_parts[1].split("Correct Answer:")
-        options = options_and_answer[0].strip()
+        with torch.no_grad():
+            outputs = model.generate(input_ids, max_length=max_length, num_return_sequences=1, do_sample=True, temperature=0.7)
         
-        answer_and_explanation = options_and_answer[1].split("Explanation:")
-        correct_answer = answer_and_explanation[0].strip()
-        explanation = answer_and_explanation[1].strip() if len(answer_and_explanation) > 1 else "No explanation provided."
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
         
-        return f"Question: {question}\n\nOptions: {options}\n\nCorrect Answer: {correct_answer}\n\nExplanation: {explanation}"
-    else:
-        return "Unable to generate a proper question and answer. Please try again with a different input."
+        # Parse the generated text
+        parts = generated_text.split("Question: ")
+        if len(parts) > 1:
+            qa_parts = parts[1].split("Options:")
+            question = qa_parts[0].strip()
+            
+            options_and_answer = qa_parts[1].split("Correct Answer:")
+            options = options_and_answer[0].strip()
+            
+            answer_and_explanation = options_and_answer[1].split("Explanation:")
+            correct_answer = answer_and_explanation[0].strip()
+            explanation = answer_and_explanation[1].strip() if len(answer_and_explanation) > 1 else "No explanation provided."
+            
+            return f"Question: {question}\n\nOptions: {options}\n\nCorrect Answer: {correct_answer}\n\nExplanation: {explanation}"
+        else:
+            return "Unable to generate a proper question and answer. Please try again with a different input."
+    except Exception as e:
+        return f"An error occurred: {str(e)}\n{traceback.format_exc()}"
 
 def slow_qa(message, history):
-    full_response = generate_qa(message)
-    for i in range(len(full_response)):
-        time.sleep(0.01)  # Adjust this value to control the speed of the response
-        yield full_response[:i+1]
+    try:
+        full_response = generate_qa(message)
+        for i in range(len(full_response)):
+            time.sleep(0.01)
+            yield full_response[:i+1]
+    except Exception as e:
+        yield f"An error occurred: {str(e)}\n{traceback.format_exc()}"
 
 # Create and launch the Gradio interface
-gr.ChatInterface(
-    slow_qa,
-    chatbot=gr.Chatbot(height=500),
-    textbox=gr.Textbox(placeholder="Enter historical text here...", container=False, scale=7),
-    title="History Q&A Generator",
-    description="Enter a piece of historical text, and the model will generate a related question, answer options, correct answer, and explanation.",
-    theme="soft",
-    examples=[
-        "The American Revolution was a colonial revolt that took place between 1765 and 1783.",
-        "World War II was a global conflict that lasted from 1939 to 1945, involving many of the world's nations.",
-        "The Renaissance was a period of cultural, artistic, political, and economic revival following the Middle Ages."
-    ],
-    cache_examples=False,
-    retry_btn="Regenerate",
-    undo_btn="Remove last",
-    clear_btn="Clear",
-).launch()
+try:
+    iface = gr.ChatInterface(
+        slow_qa,
+        chatbot=gr.Chatbot(height=500),
+        textbox=gr.Textbox(placeholder="Enter historical text here...", container=False, scale=7),
+        title="History Q&A Generator",
+        description="Enter a piece of historical text, and the model will generate a related question, answer options, correct answer, and explanation.",
+        theme="soft",
+        examples=[
+            "The American Revolution was a colonial revolt that took place between 1765 and 1783.",
+            "World War II was a global conflict that lasted from 1939 to 1945, involving many of the world's nations.",
+            "The Renaissance was a period of cultural, artistic, political, and economic revival following the Middle Ages."
+        ],
+        cache_examples=False,
+        retry_btn="Regenerate",
+        undo_btn="Remove last",
+        clear_btn="Clear",
+    )
+    
+    if error_message:
+        print("Launching interface with error message.")
+        iface.launch(debug=True)
+    else:
+        print("Launching interface normally.")
+        iface.launch(debug=True)
+except Exception as e:
+    print(f"An error occurred while creating or launching the interface: {str(e)}\n{traceback.format_exc()}")