Update app.py

app.py

@@ -1,23 +1,63 @@
 import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-# The URL of your deployed Modal app
-MODAL_URL = "https://seifosama708--code-optimizer-app-web.modal.run"
-
-# Using an iframe to embed the Modal app directly
-# This is the most reliable way to display an external web server in a Space
-with gr.Blocks(fill_height=True) as demo:
-    gr.HTML(
-        f"""
-        <iframe 
-            src="{MODAL_URL}" 
-            width="100%" 
-            height="100%" 
-            style="border:none; min-height:800px;" 
-            allow="accelerometer; ambient-light-sensor; camera; encrypted-media; geolocation; gyroscope; hid; microphone; midi; payment; usb; vr; xr-spatial-tracking" 
-            sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads"
-        ></iframe>
-        """
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+base_model_id = "Qwen/Qwen2.5-Coder-7B-Instruct"
+adapter_repo_id = "SeifElden2342532/Code-Optimizer"
+
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(model, adapter_repo_id)
+model = model.merge_and_unload()
+model.eval()
+print("Model ready!")
+
+SYSTEM_PROMPT = "You are an expert Python code optimizer. Your goal is to take user-provided Python code and optimize it for performance, readability, or conciseness, based on the user's specified category. Provide the optimized code, a brief explanation of the changes, and a complexity comparison table (e.g., time and space complexity before and after optimization)."
+
+def optimize(code, category):
+    if not code.strip():
+        return "Please enter some Python code."
+
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": f"Original Code:\n```python\n{code}\n```\nCategory: {category}"}
+    ]
+
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
+
+    # Strip input tokens from output
+    input_len = model_inputs["input_ids"].shape[1]
+    output_ids = generated_ids[0][input_len:]
+    return tokenizer.decode(output_ids, skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=optimize,
+    inputs=[
+        gr.Code(language="python", label="Your Python Code", lines=15),
+        gr.Radio(
+            choices=["Performance", "Readability", "Conciseness"],
+            value="Performance",
+            label="Optimization Category"
+        )
+    ],
+    outputs=gr.Textbox(label="Optimized Code & Explanation", lines=20),
+    title="⚡ Python Code Optimizer",
+    description="A QLoRA fine-tuned Qwen2.5-Coder-7B model that optimizes your Python code for performance, readability, or conciseness.",
+    examples=[
+        ["def factorial(n):\n    if n == 0:\n        return 1\n    else:\n        return n * factorial(n-1)", "Performance"],
+        ["result = []\nfor i in range(10):\n    result.append(i * 2)", "Conciseness"],
+    ],
+    flagging_mode="never"
+)
+
+demo.launch()