Update app.py

app.py

@@ -1,3 +1,80 @@
-import gradio as gr
+# from docx import Document
+# from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
+# import torch
+# import gradio as gr
 
-gr.load("models/kuttersn/gpt2_chatbot").launch()
+# # Load the Word document
+# docx_file_path = "Our Leadership.docx"
+# doc = Document(docx_file_path)
+
+# # Extract text from the document
+# text = ""
+# for paragraph in doc.paragraphs:
+#     text += paragraph.text + "\n"
+
+# # Save the extracted text to a text file
+# txt_file_path = "extracted_text.txt"
+# with open(txt_file_path, "w", encoding="utf-8") as file:
+#     file.write(text)
+
+# # Load the pre-trained GPT-2 model and tokenizer
+# tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+# model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+# # Tokenize the training data
+# input_ids = tokenizer(text, return_tensors="pt", padding=True, truncation=True)["input_ids"]
+
+# # Define the training arguments
+# training_args = TrainingArguments(
+#     per_device_train_batch_size=4,
+#     num_train_epochs=3,
+#     logging_dir='./logs',
+# )
+
+# # Define a dummy data collator (required by Trainer)
+# class DummyDataCollator:
+#     def __call__(self, features):
+#         return features
+
+# # Define a Trainer instance
+# trainer = Trainer(
+#     model=model,
+#     args=training_args,
+#     data_collator=DummyDataCollator(),
+#     train_dataset=input_ids
+# )
+
+# # Train the model
+# trainer.train()
+
+# # Define the chatbot function
+# def chatbot(input_text):
+#     # Tokenize input text
+#     input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+#     # Generate response from the model
+#     output_ids = model.generate(input_ids, max_length=50, pad_token_id=tokenizer.eos_token_id)
+    
+#     # Decode the generated response
+#     response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+#     return response
+
+# # Create the Gradio interface
+# chatbot_interface = gr.Interface(chatbot, "textbox", "textbox", title="Chatbot")
+
+# # Launch the Gradio interface
+# chatbot_interface.launch()
+
+
+import os
+
+# Get the current working directory
+current_directory = os.getcwd()
+
+# Construct the full file path
+docx_file_name = "Our Leadership.docx"
+full_file_path = os.path.join(current_directory, docx_file_name)
+
+# Print the file path
+print("File path:", full_file_path)