app.py

import os
import json
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)
    return tokenizer, model

# Load the model once at startup
tokenizer, model = load_model()

def evaluate_tinyllama(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."}
    return result

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.
    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.
Language: {language}
Problem: "{question}"
Solution: "{code}"
Return ONLY valid JSON: {{"stars": number, "feedback": string}}.
Do not include any extra text.
"""
    result = evaluate_tinyllama(prompt)
    return f"Stars: {result.get('stars', 0)}\nFeedback: {result.get('feedback', '')}"

iface = gr.Interface(
    fn=evaluate_code,
    inputs=[
        gr.Dropdown(choices=["C", "Python", "Java"], label="Language"),
        gr.Textbox(lines=2, placeholder="Enter the problem question here...", label="Question"),
        gr.Code(language="python", label="Your Code")
    ],
    outputs=gr.Textbox(label="Evaluation Result"),
    title="Code Evaluator",
    description="Enter a coding question and your solution to get AI-powered feedback. Supports C, Python, and Java."
)

if __name__ == "__main__":
    iface.launch()