Update app.py

app.py

@@ -1,31 +1,56 @@
 import gradio as gr
+import json
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+def load_model():
+    # Change to the actual TinyLlama model identifier available on Hugging Face.
+    model_name = "TheBloke/tiny-llama-7b"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+# Load the model once when the app starts
+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:
+        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):
-    # Check if code is provided
     if not code.strip():
         return "Error: No code provided. Please enter your solution code."
-
-    # Dummy evaluation logic based on language.
-    # Replace this with your TinyLlama integration later.
-    if language.lower() == "c":
-        return "C evaluation: The code meets basic criteria but may need improvements."
-    elif language.lower() == "python":
-        return "Python evaluation: Code is correct and follows best practices."
-    elif language.lower() == "java":
-        return "Java evaluation: Good structure, but consider error handling improvements."
-    else:
-        return "Unsupported language. Please select C, Python, or Java."
+    
+    # Build a detailed prompt for the 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)
+    # Format the output nicely
+    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(label="Your Code (if any)")  # No placeholder property here.
+        gr.Code(language="python", label="Your Code")
     ],
     outputs=gr.Textbox(label="Evaluation Result"),
     title="Code Evaluator",
-    description="Enter a coding question and your solution (if available) to get AI-powered feedback. Supports C, Python, and Java."
+    description="Enter a coding question and your solution to get AI-powered feedback. Supports C, Python, and Java."
 )
 
 if __name__ == "__main__":