Update app.py

app.py

@@ -1,28 +1,81 @@
-import streamlit as st
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
+from peft import LoraConfig, get_peft_model, TaskType
+from huggingface_hub import HfApi, Repository
+from datasets import load_dataset
+
+# Hugging Face details
+HF_TOKEN = "your_huggingface_token"
+REPO_NAME = "tinyllama-lora-finetuned"
+
+# Initialize Streamlit
+st.title("🧑‍🏫 Python Tutor AI (Fine-tuned with LoRA)")
+
+# Create HF repo if it doesn't exist
+api = HfApi()
+api.create_repo(REPO_NAME, token=HF_TOKEN, repo_type="model", exist_ok=True)
+repo = Repository(local_dir=REPO_NAME, clone_from=f"hf://{REPO_NAME}", use_auth_token=HF_TOKEN)
 
-# Load fine-tuned model
-model_path = "tinyllama-lora-finetuned"
-st.write("Loading fine-tuned TinyLlama... (CPU Mode)")
+# Load TinyLlama Model & Tokenizer
+MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
 
-model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cpu")
-tokenizer = AutoTokenizer.from_pretrained(model_path)
+# LoRA Configuration
+lora_config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    inference_mode=False,
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.1
+)
 
-st.title("Fine-Tuned TinyLlama Chatbot (LoRA)")
-st.write("🚀 Chatbot trained with LoRA on CPU.")
+# Apply LoRA to Model
+model = get_peft_model(model, lora_config)
 
-user_input = st.text_area("Enter your prompt:", "")
+# Load dataset for fine-tuning
+dataset = load_dataset("Abirate/english_python_code_instructions", split="train[:2%]")
 
-if st.button("Generate Response"):
+# Fine-Tuning Parameters
+training_args = TrainingArguments(
+    output_dir="./results",
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=4,
+    optim="adamw_torch",
+    num_train_epochs=1,
+    logging_steps=10,
+    save_strategy="no"
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset
+)
+
+# Fine-tune Model
+st.write("🎯 Fine-tuning Model (LoRA)...")
+trainer.train()
+st.success("✅ Fine-tuning complete!")
+
+# Push model to Hugging Face
+model.push_to_hub(REPO_NAME, use_auth_token=HF_TOKEN)
+tokenizer.push_to_hub(REPO_NAME, use_auth_token=HF_TOKEN)
+st.success("🚀 Model pushed to Hugging Face!")
+
+# User Input for Python Tutoring
+user_input = st.text_area("📝 Ask me a Python question:")
+if st.button("Get Answer"):
     if user_input:
-        with st.spinner("Generating response..."):
-            inputs = tokenizer(user_input, return_tensors="pt").to("cpu")
-            output = model.generate(**inputs, max_length=100)
-            response = tokenizer.decode(output[0], skip_special_tokens=True)
-            st.write("**Response:**")
-            st.write(response)
+        inputs = tokenizer(user_input, return_tensors="pt").to("cuda")
+        outputs = model.generate(**inputs, max_new_tokens=100)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.write("💡 AI Tutor:", response)
     else:
-        st.warning("Please enter a prompt!")
-
-st.write("✅ Running on CPU - May be slow.")
+        st.warning("⚠️ Please enter a question.")