Update app.py

app.py

@@ -1,16 +1,24 @@
 import gradio as gr
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
 import torch
 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("path_to_your_model_directory")
+# Load the tokenizer (you can use the pre-trained tokenizer for GPT-2 family)
+tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
 
-# Load the model from the directory containing 'pytorch_model.bin' and 'config.json'
-model = GPT2LMHeadModel.from_pretrained("path_to_your_model_directory")
+# Manually create a configuration for the model (since we don't have config.json)
+config = GPT2Config.from_pretrained("distilgpt2")
+
+# Initialize the model using the manually created configuration
+model = GPT2LMHeadModel(config)
+
+# Load the weights from the pytorch_model.bin file
+model_path = "./pytorch_model.bin"  # Path to your local model file
+state_dict = torch.load(model_path, map_location=device)  # Load the state_dict
+model.load_state_dict(state_dict)  # Load the state dict into the model
 
 # Move model to the device (GPU or CPU)
 model.to(device)
@@ -66,3 +74,4 @@ interface.launch()
 
 
 
+