Update app.py

app.py

@@ -4,7 +4,6 @@ import re
 import gradio as gr
 import torch
 import transformers
-import requests
 
 import json
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
@@ -14,21 +13,13 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 # Define a function for generating text based on a prompt using the fine-tuned GPT-2 model and the tokenizer
 def generate_text(prompt, length=100, theme=None, **kwargs):
 
-    model_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/pytorch_model.bin"
-    config_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/config.json"
+    model_name = "sailormars18/Yelp-reviews-usingGPT2"
     
-    # Download the model and configuration files
-    model_path = "./pytorch_model.bin"
-    config_path = "./config.json"
-    
-    download_file(model_url, model_path)
-    download_file(config_url, config_path)
-    
-    # Load the model from the downloaded files
-    model = transformers.GPT2LMHeadModel.from_pretrained(model_path, config=config_path).to(device)
+    # Load the model from the Hugging Face space
+    model = GPT2LMHeadModel.from_pretrained(model_name).to(device)
     
     # Load the tokenizer from the Hugging Face space
-    tokenizer = transformers.GPT2Tokenizer.from_pretrained('gpt2')
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
     # If a theme is specified, add it to the prompt as a prefix for a special token
     if theme:
@@ -70,13 +61,6 @@ def generate_text(prompt, length=100, theme=None, **kwargs):
 
     return generated_text
 
-def download_file(url, save_path):
-    response = requests.get(url, stream=True)
-    response.raise_for_status()
-    with open(save_path, "wb") as f:
-        for chunk in response.iter_content(chunk_size=8192):
-            f.write(chunk)
-
 # Define a Gradio interface for the generate_text function
 iface = gr.Interface(
     fn=generate_text,