Update app.py

app.py

@@ -2,49 +2,43 @@ import gradio as gr
 from huggingface_hub import hf_hub_download
 from ctransformers import AutoModelForCausalLM
 
-# Download GGUF model (public, no token required)
 model_path = hf_hub_download(
-    repo_id="lmstudio-community/DeepSeek-Coder-V2-Lite-Base-GGUF",
-    filename="DeepSeek-Coder-V2-Lite-Base-Q2_K.gguf"
+    repo_id="TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF",
+    filename="tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
 )
 
-# Load model
 llm = AutoModelForCausalLM.from_pretrained(
     model_path,
-    model_type="deepseek",
-    context_length=4096
+    model_type="llama",
+    context_length=4096    # <-- correct way for ctransformers
 )
 
 def evaluate(code):
-    prompt = f"""
-You are an AI programming evaluator.
+    prompt = f"""You are a code evaluator teacher.
 
-Analyze the following Python code and provide a detailed report:
-
-1. What the program does
-2. Logic correctness
+Evaluate this Python code and give:
+1. What it does
+2. Correctness of logic
 3. Time complexity
 4. Space complexity
-5. Edge cases that may fail
-6. Performance bottlenecks
-7. Code quality / readability issues
-8. Plagiarism risk (low / medium / high)
-9. Suggestions for improvement
-10. Improved version if useful
+5. Performance issues
+6. Code quality problems
+7. Plagiarism risk level
+8. Suggestions to improve
+9. Improved version (if needed)
 
 Code:
 {code}
 """
     return llm(
         prompt,
-        max_new_tokens=1200,
-        temperature=0.2,
-        top_p=0.9
+        max_new_tokens=4096,   # tokens to generate
+        temperature=0.3
     )
 
 gr.Interface(
     fn=evaluate,
-    inputs=gr.Textbox(lines=12, label="Student Python Code"),
-    outputs=gr.Textbox(lines=22, label="AI Evaluation Report"),
-    title="DeepSeek-Coder V2 Lite 1.3B Q2 – Code Evaluator"
+    inputs="text",
+    outputs="text",
+    title="Code Evaluation (TinyLlama 1.1B – 4096 tokens)"
 ).launch()