Create app.py

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+st.title("Custom AI Model Training on Hugging Face Space")
+
+dataset_name = st.text_input("Enter Dataset Name (e.g., prasenjeetz/IQ-Dataset)")
+model_name = st.text_input("Enter Pretrained Model Name (e.g., bert-base-uncased)")
+
+if st.button("Start Training"):
+    with st.spinner("Loading Dataset..."):
+        dataset = load_dataset(dataset_name)
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+    tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+    training_args = TrainingArguments(
+        output_dir="./results",
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        learning_rate=2e-5,
+        per_device_train_batch_size=8,
+        per_device_eval_batch_size=8,
+        num_train_epochs=3,
+        weight_decay=0.01,
+    )
+
+    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["test"],
+    )
+
+    trainer.train()
+    st.success("Training Complete!")