Update app.py

app.py

@@ -4,24 +4,20 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 def load_model():
-    model_name = "TheBloke/tiny-llama-7b"
-    token = os.environ.get("HF_TOKEN")
-    if not token:
-        raise ValueError("HF_TOKEN not found in environment variables.")
-    # Load the tokenizer and model using the provided token
-    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
-    model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=token)
+    # Use a public, open-source model for code evaluation.
+    model_name = "Salesforce/codegen-350M-mono"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
     return tokenizer, model
 
-# Load the model once at startup
+# Load the model once at startup.
 tokenizer, model = load_model()
 
-def evaluate_tinyllama(prompt):
+def evaluate_model(prompt):
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_new_tokens=150)
     response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
     try:
-        # Try to parse the model's output as JSON
         result = json.loads(response_text.strip())
     except Exception as e:
         result = {"stars": 0, "feedback": "Evaluation failed. Unable to parse AI response."}
@@ -31,7 +27,7 @@ def evaluate_code(language, question, code):
     if not code.strip():
         return "Error: No code provided. Please enter your solution code."
     
-    # Build a detailed prompt for the AI evaluator.
+    # Build the prompt for the evaluation model.
     prompt = f"""
 You are an expert code evaluator.
 Rate the following solution on a scale of 0-5 (0 = completely incorrect, 5 = excellent) and provide a concise feedback message.
@@ -41,7 +37,7 @@ Solution: "{code}"
 Return ONLY valid JSON: {{"stars": number, "feedback": string}}.
 Do not include any extra text.
 """
-    result = evaluate_tinyllama(prompt)
+    result = evaluate_model(prompt)
     return f"Stars: {result.get('stars', 0)}\nFeedback: {result.get('feedback', '')}"
 
 iface = gr.Interface(