added app.py

app.py

@@ -0,0 +1,127 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "Neo111x/Falcon3-3B-Instruct-RL-CODE-FIX",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "Neo111x/Falcon3-3B-Instruct-RL-CODE-FIX",
+    trust_remote_code=True
+)
+model.eval()
+
+# Inference function
+def repair_code(faulty_code):
+    PROGRAM_REPAIR_TEMPLATE = f"""
+You are an expert in the field of software testing. 
+You are given a buggy Python program, you are supposed to first generate testcases that can expose the bug, 
+and then generate the corresponding fixed code. The two tasks are detailed as follows.
+
+1. **Generate a comprehensive set of test cases to expose the bug**:
+   - Each test case should include an input and the expected output.
+   - Output the test cases as a JSON list, where each entry is a dictionary with keys "test_input" and "test_output".
+   - Write in 
+
+json
+
+ block.
+
+2. **Provide a fixed version**:
+   - Write a correct Python program to fix the bug.
+   - Write in 
+
+python
+
+ block.
+   - The code should read from standard input and write to standard output, matching the input/output format specified in the problem.
+   
+Here is an example. 
+The faulty Python program is:
+
+python
+\"\"\"Please write a Python program to sum two integer inputs\"\"\"
+def add (x, y):
+    return x - y 
+x = int(input())
+y = int(input())
+print(add(x,y))
+
+
+
+Testcases that can expose the bug:
+
+json
+[
+    {{
+        \"test_input\":\"1\n2\",
+        \"test_output\":\"3\"
+    }},
+    {{
+        \"test_input\":\"-1\n1\",
+        \"test_output\":\"0\"
+    }},
+    {{
+        \"test_input\":\"-1\n2\",
+        \"test_output\":\"1\"
+    }}
+]
+
+
+
+Fixed code:
+
+python
+def add (x, y):
+    return x + y 
+x = int(input())
+y = int(input())
+print(add(x,y))
+
+
+
+Now, you are given a faulty Python function, please return:
+1. **Testcases** that helps expose the bug.
+2. **Fixed code** that can pass all testcases.
+
+The faulty function is:
+
+python
+{faulty_code}
+
+
+<|assistant|>
+"""
+    messages = [
+        {
+            "role": "user",
+            "content": PROGRAM_REPAIR_TEMPLATE
+        }
+    ]
+    
+    text = tokenizer.apply_chat_template(messages, tokenize=False)
+    inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            do_sample=False
+        )
+
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs["input_ids"], outputs)
+    ]
+    result = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return result.strip()
+
+# Gradio UI
+gr.Interface(
+    fn=repair_code,
+    inputs=gr.Textbox(label="Faulty Python Function", lines=15, placeholder="Paste your buggy function here..."),
+    outputs=gr.Textbox(label="Generated Test Cases and Fixed Code", lines=30),
+    title="🧠 AI Program Repair - Falcon3 3B GRPO",
+    description="Paste a buggy Python function. The model will generate test cases and a fixed version of the code."
+).launch()