Create app.py

app.py

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+#!/usr/bin/env python3
+
+"""
+Requirements:
+streamlit
+torch
+pandas
+transformers
+"""
+
+import os
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.utils.data import Dataset, DataLoader
+import csv
+
+# Page Configuration
+st.set_page_config(
+    page_title="SFT Model Builder 🚀",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Help Documentation as a Variable
+HELP_DOC = """
+# SFT Model Builder - Help Guide 🚀
+
+## Overview
+This Streamlit app allows users to **download, fine-tune, and test Transformer models** with **Supervised Fine-Tuning (SFT)** using CSV data. It is designed for NLP tasks and can be expanded for **CV and Speech models**.
+
+## Features
+- ✅ **Download a pre-trained model** from Hugging Face.
+- ✅ **Upload a CSV dataset** for fine-tuning.
+- ✅ **Train the model** with multiple epochs and adjustable batch sizes.
+- ✅ **Test the fine-tuned model** with text prompts.
+
+## Installation
+To run the app, install dependencies:
+```bash
+pip install -r requirements.txt
+```
+Then, start the app:
+```bash
+streamlit run app.py
+```
+
+## How to Use
+1. **Download Model**: Select a base model (e.g., `distilgpt2`), then click **Download Model**.
+2. **Upload CSV**: The CSV must have two columns: `prompt` and `response`.
+3. **Fine-Tune Model**: Click **Fine-Tune Model** to start training.
+4. **Test Model**: Enter a text prompt and generate responses.
+
+## CSV Format
+Example format:
+```csv
+prompt,response
+"What is AI?","AI is artificial intelligence."
+"Explain machine learning","Machine learning is a subset of AI."
+```
+
+## Model References
+| Model 🏆 | Description 📌 | Link 🔗 |
+|---------|-------------|---------|
+| **GPT-2** 🤖 | Standard NLP model | [Hugging Face](https://huggingface.co/gpt2) |
+| **DistilGPT-2** ⚡ | Lightweight version of GPT-2 | [Hugging Face](https://huggingface.co/distilgpt2) |
+| **EleutherAI Pythia** 🔬 | Open-source GPT-like models | [Hugging Face](https://huggingface.co/EleutherAI/pythia-70m) |
+
+## Additional Notes
+- This app supports **PyTorch models**.
+- Default training parameters: `epochs=3`, `batch_size=4`.
+- Fine-tuned models are **saved locally** for future use.
+
+For more details, visit [Hugging Face Models](https://huggingface.co/models). 🚀
+"""
+
+# Custom Dataset for Fine-Tuning
+class SFTDataset(Dataset):
+    def __init__(self, data, tokenizer, max_length=128):
+        self.data = data
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        prompt = self.data[idx]["prompt"]
+        response = self.data[idx]["response"]
+        input_text = f"{prompt} {response}"
+        encoding = self.tokenizer(
+            input_text,
+            max_length=self.max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt"
+        )
+        return {
+            "input_ids": encoding["input_ids"].squeeze(),
+            "attention_mask": encoding["attention_mask"].squeeze(),
+            "labels": encoding["input_ids"].squeeze()
+        }
+
+# Model Loader and Trainer Class
+class ModelBuilder:
+    def __init__(self, model_name="distilgpt2"):
+        self.model_name = model_name
+        self.model = None
+        self.tokenizer = None
+
+    def load_model(self):
+        st.spinner("Loading model... ⏳")
+        self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+        st.success("Model loaded! ✅")
+
+    def fine_tune(self, csv_path, epochs=3, batch_size=4):
+        """Supervised Fine-Tuning with CSV data"""
+        sft_data = []
+        with open(csv_path, "r") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                sft_data.append({"prompt": row["prompt"], "response": row["response"]})
+
+        dataset = SFTDataset(sft_data, self.tokenizer)
+        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+        optimizer = torch.optim.AdamW(self.model.parameters(), lr=2e-5)
+
+        self.model.train()
+        for epoch in range(epochs):
+            st.spinner(f"Training epoch {epoch + 1}/{epochs}... ⚙️")
+            for batch in dataloader:
+                optimizer.zero_grad()
+                input_ids = batch["input_ids"].to(self.model.device)
+                attention_mask = batch["attention_mask"].to(self.model.device)
+                labels = batch["labels"].to(self.model.device)
+                outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+                loss = outputs.loss
+                loss.backward()
+                optimizer.step()
+            st.write(f"Epoch {epoch + 1} completed.")
+        st.success("Fine-tuning completed! 🎉")
+
+# Main UI
+st.title("SFT Model Builder 🤖🚀")
+model_builder = ModelBuilder()
+
+if st.button("Download Model ⬇️"):
+    model_builder.load_model()
+
+csv_file = st.file_uploader("Upload CSV for Fine-Tuning", type="csv")
+if csv_file and st.button("Fine-Tune Model 🔄"):
+    model_builder.fine_tune(csv_file)
+
+# Render Help Documentation at End
+st.markdown(HELP_DOC)