Update app.py

app.py

@@ -1,25 +1,16 @@
 import gradio as gr
-from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoModelForSeq2SeqLM, AutoTokenizer, GPT2Config
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
 import torch
-from safetensors.torch import load_file as safetensors_load_file  # Import safetensors loading function
 from langchain.memory import ConversationBufferMemory
 
 # Move model to device (GPU if available)
 device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
 # Load the tokenizer (use pre-trained tokenizer for GPT-2 family)
-tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
+tokenizer = GPT2Tokenizer.from_pretrained("path_to_your_model_directory")
 
-# Load the configuration for the model (DistilGPT2 is a smaller GPT-2)
-config = GPT2Config.from_pretrained("distilgpt2")
-
-# Initialize the model using the configuration
-model = GPT2LMHeadModel(config)
-
-# Load the weights from the safetensors file
-model_path = "./model.safetensors"  # Path to your local model file
-state_dict = safetensors_load_file(model_path)  # Use safetensors loader
-model.load_state_dict(state_dict)  # Load the state dict into the model
+# Load the model from the directory containing 'pytorch_model.bin' and 'config.json'
+model = GPT2LMHeadModel.from_pretrained("path_to_your_model_directory")
 
 # Move model to the device (GPU or CPU)
 model.to(device)
@@ -74,3 +65,4 @@ interface.launch()
 
 
 
+