Update app.py

app.py

@@ -1,15 +1,11 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-
 def respond(
     message,
-    history: list[tuple[str, str]],
+    history,
     system_message,
     max_tokens,
     temperature,
@@ -17,11 +13,10 @@ def respond(
 ):
     messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Updated for OpenAI-style format (replacing tuples)
+    for entry in history:
+        role = "user" if entry["role"] == "user" else "assistant"
+        messages.append({"role": role, "content": entry["content"]})
 
     messages.append({"role": "user", "content": message})
 
@@ -35,58 +30,39 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Updated ChatInterface with correct type
 demo = gr.ChatInterface(
     respond,
-    chatbot=gr.Chatbot(type="messages"),
+    chatbot=gr.Chatbot(type="messages"),  # Correct format
     additional_inputs=[
         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()
 
-# Fine-Tuning GPT-2 on Hugging Face Spaces (Streaming 40GB Dataset, No Storage Issues)
-
-# Install required libraries
-# Install required libraries (Run this separately in a terminal or notebook cell)
-# !pip install transformers datasets peft accelerate bitsandbytes torch torchvision torchaudio gradio -q
-
+# -----------------------------------------------
+# Fine-Tuning GPT-2 on Hugging Face Spaces (Improved Section)
+# -----------------------------------------------
 from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
 from datasets import load_dataset
 from peft import LoraConfig, get_peft_model
 import torch
 
-# Authenticate Hugging Face
 from huggingface_hub import notebook_login
 notebook_login()
 
-# Load GPT-2 model and tokenizer
 model_name = "gpt2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Load the OpenWebText dataset using streaming (No download required)
-
-# Custom Dataset (Predefined Q&A Pairs for Project Expo)
 custom_data = [
     {"prompt": "Who are you?", "response": "I am Eva, a virtual voice assistant."},
     {"prompt": "What is your name?", "response": "I am Eva, how can I help you?"},
@@ -97,30 +73,22 @@ custom_data = [
     {"prompt": "Who created you?", "response": "I was created by an expert team specializing in AI fine-tuning and web development."}
 ]
 
-# Convert custom dataset to Hugging Face Dataset
 dataset_custom = load_dataset("json", data_files={"train": custom_data})
+dataset = load_dataset("Skylion007/openwebtext", split="train[:20%]")
 
-# Merge with OpenWebText dataset
-dataset = load_dataset("Skylion007/openwebtext", split="train[:20%]")  # Load 5% to avoid streaming issues
-
-# Tokenization function
 def tokenize_function(examples):
     return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
 
 tokenized_datasets = dataset.map(tokenize_function, batched=True)
 
-# Apply LoRA for efficient fine-tuning
 lora_config = LoraConfig(
     r=8, lora_alpha=32, lora_dropout=0.05, bias="none",
-    target_modules=["c_attn", "c_proj"]  # Apply LoRA to attention layers
+    target_modules=["c_attn", "c_proj"]
 )
 
 model = get_peft_model(model, lora_config)
-
-# Enable gradient checkpointing to reduce memory usage
 model.gradient_checkpointing_enable()
 
-# Training arguments
 training_args = TrainingArguments(
     output_dir="gpt2_finetuned",
     auto_find_batch_size=True,
@@ -133,22 +101,17 @@ training_args = TrainingArguments(
     push_to_hub=True
 )
 
-# Trainer setup
 trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_datasets
 )
 
-# Start fine-tuning
 trainer.train()
-
-# Save and push the model to Hugging Face Hub
 trainer.save_model("gpt2_finetuned")
 tokenizer.save_pretrained("gpt2_finetuned")
 trainer.push_to_hub()
 
-# Deploy as Gradio Interface
 def generate_response(prompt):
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_length=100)