Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+from datasets import load_dataset
+import os
+from transformers import set_seed
+
+# Streamlit App title
+st.title("Fine-Tune Mixtral 8x7B Model")
+
+# Model name input field
+model_name = "mistral-8x7b"  # Use the Mixtral 8x7B model
+
+# Access the Hugging Face token from Streamlit secrets
+token = st.secrets["HF_TOKEN"]
+
+# Load the tokenizer and model
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
+    model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=token)
+    st.write("Model and tokenizer loaded successfully!")
+except Exception as e:
+    st.error(f"An error occurred while loading the model: {e}")
+
+# Load the dataset from the existing file
+dataset_path = "training_data.json"
+
+try:
+    dataset = load_dataset('json', data_files={'train': dataset_path})
+    st.write("Dataset loaded successfully!")
+except Exception as e:
+    st.error(f"An error occurred while loading the dataset: {e}")
+
+# Tokenize the dataset
+def preprocess_function(examples):
+    return tokenizer(examples['prompt'], truncation=True, padding="max_length", max_length=128)
+
+try:
+    tokenized_dataset = dataset['train'].map(preprocess_function, batched=True)
+    st.write("Dataset tokenized successfully!")
+except Exception as e:
+    st.error(f"An error occurred while tokenizing the dataset: {e}")
+
+# Training arguments for fine-tuning
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=1,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+
+# Initialize the Trainer for fine-tuning
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+)
+
+# Button to start fine-tuning
+if st.button("Start Fine-Tuning"):
+    with st.spinner("Fine-tuning in progress..."):
+        try:
+            trainer.train()
+            st.success("Fine-tuning completed!")
+            # Save the fine-tuned model
+            model.save_pretrained("./fine_tuned_model")
+            st.write("Fine-tuned model saved!")
+        except Exception as e:
+            st.error(f"An error occurred during fine-tuning: {e}")