Create app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from unsloth import FastLanguageModel
+import numpy as np
+
+# Load fine-tuned model
+model_name = "sue888888888888/essay_grader"
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+FastLanguageModel.for_inference(model)
+
+# Prompt template
+prompt_template = """Below is an instruction that describes how to grade an essay, paired with an input that provides the grading schema. Write a response that grades essays based on the mark schema provided.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input_text}
+
+### Response:
+"""
+
+def grade_essay(question, reference, student, mark1, mark2, mark3, mark4):
+    mark_scheme = {
+        "1": mark1,
+        "2": mark2,
+        "3": mark3,
+        "4": mark4
+    }
+
+    instruction = "Grade this essay based on the following mark scheme:\n" + "\n".join([f"Criterion {k}: {v}" for k, v in mark_scheme.items()])
+    input_text = f"Question: {question}\nReference Answer: {reference}\nStudent Answer: {student}"
+    full_prompt = prompt_template.format(instruction=instruction, input_text=input_text)
+
+    inputs = tokenizer([full_prompt], return_tensors="pt").to("cuda")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=50,
+            temperature=0.3,
+            do_sample=True,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    response = decoded.split("### Response:")[-1].strip()
+
+    return response
+
+# UI
+demo = gr.Interface(
+    fn=grade_essay,
+    inputs=[
+        gr.Textbox(label="Question"),
+        gr.Textbox(label="Reference Answer"),
+        gr.Textbox(label="Student Answer"),
+        gr.Textbox(label="Marking Criterion 1"),
+        gr.Textbox(label="Marking Criterion 2"),
+        gr.Textbox(label="Marking Criterion 3"),
+        gr.Textbox(label="Marking Criterion 4"),
+    ],
+    outputs=gr.Textbox(label="Model Response (Score or Explanation)"),
+    title="📝 Essay Grader (Mistral + Unsloth)"
+)
+
+demo.launch()