Create app.py

app.py

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+import gradio as gr
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# --- CONFIGURATION ---
+# The base model 
+BASE_MODEL = "unsloth/llama-3-8b" 
+# Fine-tuned adapter
+ADAPTER_MODEL = "nihardon/fine-tuned-unit-test-generator" 
+
+print(f"Loading {ADAPTER_MODEL} on CPU... (This might take a minute)")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+
+# Load model (CPU optimized loading)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    device_map="cpu",
+    torch_dtype=torch.float32, 
+    low_cpu_mem_usage=True
+)
+
+# Apply fine-tuned adapters
+model = PeftModel.from_pretrained(model, ADAPTER_MODEL)
+
+def generate_test(user_code):
+    alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+You are an expert Python QA engineer. Write a pytest unit test for the following function.
+
+### Input:
+{}
+
+### Response:
+"""
+    # Format and tokenize
+    prompt = alpaca_prompt.format(user_code)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    # Generate
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, 
+            max_new_tokens=256, 
+            use_cache=True,
+            temperature=0.1 
+        )
+    
+    # Decode
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("### Response:")[-1].strip()
+
+# UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧪 AI Unit Test Generator")
+    gr.Markdown(f"**Model:** {ADAPTER_MODEL} (Llama-3 Fine-Tune) | **Status:** Running on CPU")
+    gr.Markdown("Paste your Python function below, and the AI will write a Pytest case for it.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_box = gr.Code(language="python", label="Paste Python Function Here", value="def add(a, b):\n    return a + b")
+            btn = gr.Button("Generate Pytest", variant="primary")
+        with gr.Column():
+            output_box = gr.Code(language="python", label="Generated Test Case")
+            
+    btn.click(generate_test, inputs=input_box, outputs=output_box)
+
+demo.launch()