Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Point this to the repository you just created in Step 1
+model_id = "lakshraina2/leetcodeAI"
+
+print("Downloading and loading model...")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+# We load in float32 because the free tier uses standard CPU instances
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
+
+def solve_problem(problem_text):
+    prompt = f"### Instruction:\nSolve this LeetCode problem:\n{problem_text}\n\n### Response:\n"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    outputs = model.generate(
+        **inputs, 
+        max_new_tokens=512,
+        temperature=0.2, 
+        do_sample=True
+    )
+    
+    solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    try:
+        code_only = solution.split("### Response:\n")[1].strip()
+    except IndexError:
+        code_only = solution
+        
+    return code_only
+
+# Gradio automatically builds an API around this function
+iface = gr.Interface(fn=solve_problem, inputs="text", outputs="text")
+iface.launch()