Update app.py

app.py

@@ -1,28 +1,33 @@
 # Import necessary libraries
+import blurr
 import gradio as gr
 from pathlib import Path
 from fastai.text.all import *
 from blurr.text.data.all import *
 from blurr.text.modeling.all import *
 
-# Load the learner with trust_remote_code=True
+# Manually download and prepare SQuAD dataset
+from datasets import load_dataset
+squad = load_dataset("squad")
+
+# Load the learner without using SQuAD
 inf_learn = load_learner(fname=Path("laptop_summarizer_1.pkl"), trust_remote_code=True)
 
 # Define a function to generate summaries using your model
 def generate_summary(input_text):
-  prediction = inf_learn.blurr_generate(input_text)
-  generated_text = prediction[0]['generated_texts']
-  return generated_text
+    prediction = inf_learn.blurr_generate(input_text)
+    generated_text = prediction[0]['generated_texts']
+    return generated_text
 
 # Create an interface for the model
 interface = gr.Interface(
-  fn=generate_summary,  # The function to generate summaries
-  inputs=gr.inputs.Textbox(),  # Input field for text
-  outputs=gr.outputs.Textbox(),  # Output field for generated text
-  live=True,  # Whether to update results in real-time
-  title="Laptop Guru",  # Title of the interface
-  description="Enter your requirements & get valuable insight from Guru."  # Description of the interface
+    fn=generate_summary,  # The function to generate summaries
+    inputs=gr.inputs.Textbox(),  # Input field for text
+    outputs=gr.outputs.Textbox(),  # Output field for generated text
+    live=True,  # Whether to update results in real-time
+    title="Laptop Guru",  # Title of the interface
+    description="Enter your requirements & get valuable insight from Guru."  # Description of the interface
 )
 
 # Start the Gradio app
-interface.launch(inline=True)
+interface.launch(inline=True, trust_remote_code=True)