actual code deployed

app.py

@@ -1,26 +1,35 @@
+
+
+# Import necessary libraries
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-# Initialize the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("laptop_data.pkl")  
-model = AutoModelForSequenceClassification.from_pretrained("laptop_data.pkl") 
-
-# Define the function for classifying laptops
-def classify_laptop(description):
-    inputs = tokenizer(description, return_tensors="pt", padding=True, truncation=True)
-    outputs = model(**inputs)
-    logits = outputs.logits
-    probabilities = torch.softmax(logits, dim=1)
-    return {label: prob.item() for label, prob in zip(model.config.id2label.values(), probabilities[0])}
-
-# Create the Gradio interface
+from pathlib import Path
+from fastai.text.all import *
+from blurr.text.data.all import *
+from blurr.text.modeling.all import *
+import pathlib
+
+# Load the learner
+inf_learn = load_learner(fname=Path("laptop_summarizer_1.pkl"))
+
+# 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
+
+
+# Create an interface for the model
 iface = gr.Interface(
-    fn=classify_laptop,
-    inputs=gr.inputs.Textboxbox(),
-    outputs=gr.outputs.Label(num_top_classes=5),
-    live=True
+    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 of the interface
+    description="Enter your requirements & get valuable insight from Guru."
 )
 
-# Launch the Gradio interface
-iface.launch()
+# Start the Gradio app
+iface.launch(inline=False)