fixedcatagory app.py

app.py

@@ -1,29 +1,47 @@
 import gradio as gr
 from transformers import pipeline
 
-# Use a small CPU-friendly model
+# ✅ Small, CPU-friendly model
 generator = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
 
+# 🧠 Stronger, structured prompt
 def build_prompt(essay_text):
-    return f"""Evaluate this essay based on the following categories (0–100):
-1. Grammar & Mechanics
-2. Coherence & Flow
-3. Clarity & Style
-4. Argument Strength & Evidence
-5. Structure & Organization
-6. Teacher-Specific Style
-
-Return scores as a JSON object, and include the average as 'Total Grade'.
-
-Essay:
-{essay_text}
+    return f"""
+You are an experienced English teacher.
+
+Evaluate the following student essay in **6 categories**, giving a score from 0 to 100 for each:
+
+1. Grammar & Mechanics  
+2. Coherence & Flow  
+3. Clarity & Style  
+4. Argument Strength & Evidence  
+5. Structure & Organization  
+6. Teacher-Specific Style  
+
+Then calculate the average of those 6 scores and return it as **"Total Grade"**.
+
+⚠️ Format your response **only** as JSON like this:
+{{
+  "Grammar & Mechanics": 87,
+  "Coherence & Flow": 92,
+  "Clarity & Style": 85,
+  "Argument Strength & Evidence": 90,
+  "Structure & Organization": 88,
+  "Teacher-Specific Style": 91,
+  "Total Grade": 88.83
+}}
+
+Here is the essay:
+\"\"\"{essay_text}\"\"\"
 """
 
+# 📄 Optional file input
 def extract_text(file):
     if file.name.endswith(".txt"):
         return file.read().decode("utf-8")
     return "Unsupported file type. Please upload a .txt file."
 
+# 💬 Main grading logic
 def grade_essay(essay, file):
     if not essay and not file:
         return "Please provide either text or a file."
@@ -35,11 +53,19 @@ def grade_essay(essay, file):
 
     prompt = build_prompt(essay)
     result = generator(prompt, max_new_tokens=256)[0]["generated_text"]
-    return result
 
+    # Extract just the JSON if the model follows the prompt
+    start = result.find("{")
+    end = result.rfind("}") + 1
+    if start != -1 and end != -1:
+        return result[start:end]
+
+    return "Model response could not be parsed as JSON."
+
+# 🎨 Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# ✏️ MimicMark – AI Essay Evaluator")
-    gr.Markdown("Paste your essay or upload a `.txt` file. The AI will score it in 6 categories.")
+    gr.Markdown("Paste your essay or upload a `.txt` file. The AI will score it in 6 categories and return a final numeric grade.")
 
     with gr.Row():
         essay_input = gr.Textbox(label="Paste Your Essay", lines=12)