Update app.py

app.py

@@ -1,19 +1,46 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
 
+# Load pre-trained model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-def chatbot(input_text):
-    # Encode the input text
-    input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
+# Prepare your dataset (replace this with your own dataset)
+dataset = [
+    ("Hello!", "Hi there!"),
+    ("How are you?", "I'm doing well, thanks!"),
+    # Add more conversational pairs here
+]
+
+# Tokenize the dataset
+tokenized_dataset = tokenizer([example[0] for example in dataset], return_tensors="pt", padding=True, truncation=True)
+
+# Fine-tune the model
+training_args = TrainingArguments(
+    per_device_train_batch_size=4,
+    num_train_epochs=3,
+    logging_dir='./logs',
+    logging_steps=100,
+)
 
-    # Generate a response from the model
-    response_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+)
 
-    # Decode the response and return it
+trainer.train()
+
+# Save the fine-tuned model
+model.save_pretrained("fine_tuned_dialogpt")
+
+# Example of using the fine-tuned model for chatbot
+def chatbot(input_text):
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    response_ids = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
     response_text = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
     return response_text
 
+# Example interaction with the fine-tuned model
 while True:
     user_input = input("You: ")
     if user_input.lower() == 'exit':