Update app.py

app.py

@@ -1,17 +1,11 @@
 import gradio as gr
 from transformers import pipeline
 
-# ✅ Use a public, CPU-compatible model
-model_name = "tiiuae/falcon-7b-instruct"
-generator = pipeline("text-generation", model=model_name, device=-1)  # CPU
+# Use a small CPU-friendly model
+generator = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
 
-# 🧠 Prompt template
 def build_prompt(essay_text):
-    return f"""You are an experienced English teacher.
-
-Your task is to evaluate the following student essay in 6 categories, each scored from 0 to 100.
-
-Categories:
+    return f"""Evaluate this essay based on the following categories (0–100):
 1. Grammar & Mechanics
 2. Coherence & Flow
 3. Clarity & Style
@@ -19,31 +13,17 @@ Categories:
 5. Structure & Organization
 6. Teacher-Specific Style
 
-Then, calculate the average of the 6 scores as the Total Grade.
+Return scores as a JSON object, and include the average as 'Total Grade'.
 
-Output format:
-Return only a JSON object like this:
-{{
-  "Grammar & Mechanics": score,
-  "Coherence & Flow": score,
-  "Clarity & Style": score,
-  "Argument Strength & Evidence": score,
-  "Structure & Organization": score,
-  "Teacher-Specific Style": score,
-  "Total Grade": average
-}}
-
-Here is the essay:
+Essay:
 {essay_text}
 """
 
-# 📄 Optional file support (txt only)
 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 evaluation function
 def grade_essay(essay, file):
     if not essay and not file:
         return "Please provide either text or a file."
@@ -54,22 +34,15 @@ def grade_essay(essay, file):
             return essay
 
     prompt = build_prompt(essay)
-    result = generator(prompt, max_new_tokens=400, temperature=0.5)[0]["generated_text"]
-
-    # Extract only the JSON part
-    start = result.find("{")
-    end = result.rfind("}") + 1
-    if start != -1 and end != -1:
-        return result[start:end]
-    return "Model response couldn't be parsed."
+    result = generator(prompt, max_new_tokens=256)[0]["generated_text"]
+    return result
 
-# 🎨 Gradio Interface
 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 and return a total grade.")
+    gr.Markdown("Paste your essay or upload a `.txt` file. The AI will score it in 6 categories.")
 
     with gr.Row():
-        essay_input = gr.Textbox(label="Paste Your Essay", lines=12, placeholder="Type or paste your essay here...")
+        essay_input = gr.Textbox(label="Paste Your Essay", lines=12)
         file_input = gr.File(label="Or Upload a .txt File", file_types=[".txt"])
 
     output = gr.Textbox(label="AI Evaluation Output", lines=15)