Update app.py

app.py

@@ -14,20 +14,20 @@ tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model.eval()
 
 # -------------------------------
-# Scoring function
+# Scoring + Feedback function
 # -------------------------------
 def score_essay(essay: str):
-    # Empty essay → zero scores
-    if not essay or not essay.strip():
+    if not essay.strip():
         return {
             "Task Achievement": 0.0,
             "Coherence & Cohesion": 0.0,
             "Vocabulary": 0.0,
             "Grammar": 0.0,
             "Overall": 0.0,
+            "Feedback": "Please write an essay to get a score."
         }
 
-    # Tokenize input, truncate to 512 tokens
+    # Tokenize input
     inputs = tokenizer(
         essay,
         return_tensors="pt",
@@ -39,45 +39,79 @@ def score_essay(essay: str):
     with torch.no_grad():
         outputs = model(**inputs)
 
-    # Raw logits from the model
     preds = outputs.logits.squeeze().cpu().numpy()
 
     # -------------------------------
-    # CORRECT CALIBRATION
+    # Refined piecewise scaling
     # -------------------------------
-    # Clip logits to 0-9 (IELTS band)
-    scores = np.clip(preds, 0, 9)
+    scores = preds.copy()
+    for i in range(len(scores)):
+        if scores[i] < 4.0:       # very weak essays
+            scores[i] = scores[i]*1.25 + 0.3
+        elif scores[i] < 6.0:     # medium essays
+            scores[i] = scores[i]*1.1 + 0.2
+        elif scores[i] < 8.0:     # good essays
+            scores[i] = scores[i]*1.05 + 0.1
+        else:                      # very strong essays
+            scores[i] = scores[i]  # keep unchanged
 
-    # Round to nearest 0.5
+    # -------------------------------
+    # Word count bonus
+    # -------------------------------
+    wc = len(essay.split())
+    if wc >= 250:
+        scores += 0.3
+    elif wc >= 200:
+        scores += 0.15
+
+    # -------------------------------
+    # Clamp and round to 0.5
+    # -------------------------------
+    scores = np.clip(scores, 0, 9)
     rounded = np.round(scores * 2) / 2
 
-    # Optional: Penalize very short essays (like IELTS examiners)
-    word_count = len(essay.split())
-    if word_count < 150:
-        rounded -= 1.0
-    elif word_count < 250:
-        rounded -= 0.5
+    ta, cc, vocab, gram, overall_dummy = rounded  # we will compute weighted overall
 
-    rounded = np.clip(rounded, 0, 9)
+    # -------------------------------
+    # Weighted overall band
+    # -------------------------------
+    overall = 0.3*ta + 0.25*cc + 0.2*vocab + 0.25*gram
+    overall = np.clip(overall, 0, 9)
+    overall = np.round(overall * 2) / 2
+    rounded[-1] = overall  # replace last element
 
-    labels = [
-        "Task Achievement",
-        "Coherence & Cohesion",
-        "Vocabulary",
-        "Grammar",
-        "Overall"
-    ]
+    labels = ["Task Achievement", "Coherence & Cohesion", "Vocabulary", "Grammar", "Overall"]
 
-    return {label: float(score) for label, score in zip(labels, rounded)}
+    # -------------------------------
+    # Dynamic mini feedback
+    # -------------------------------
+    feedback_lines = []
+
+    if ta < 5:
+        feedback_lines.append("Task: Ideas are underdeveloped; give examples and elaboration.")
+    if cc < 5:
+        feedback_lines.append("Coherence: Improve transitions; use connectors like 'however', 'moreover'.")
+    if vocab < 5:
+        feedback_lines.append("Vocabulary: Use a wider range of words and avoid repetition.")
+    if gram < 5:
+        feedback_lines.append("Grammar: Check articles, plurals, tenses, and sentence structures.")
+    if overall >= 5:
+        feedback_lines.append("Overall: Good attempt! Refine grammar and vocabulary to increase your band.")
+
+    feedback = "\n".join(feedback_lines[:4])  # max 4 lines
+
+    result = {label: float(score) for label, score in zip(labels, rounded)}
+    result["Feedback"] = feedback
+    return result
 
 # -------------------------------
 # Gradio UI
 # -------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 📝 Automated IELTS Writing Scorer")
+    gr.Markdown("## 📝 Refined Automated IELTS Writing Scorer")
     gr.Markdown(
         "Paste your IELTS Task 2 essay below. "
-        "The system will estimate band scores for all four criteria and the overall band."
+        "The system estimates band scores with mini feedback."
     )
 
     essay_input = gr.Textbox(
@@ -85,10 +119,9 @@ with gr.Blocks() as demo:
         placeholder="Paste your IELTS essay here..."
     )
 
-    score_output = gr.JSON(label="Estimated IELTS Band Scores")
+    score_output = gr.JSON(label="Estimated IELTS Band Scores + Feedback")
 
     submit_btn = gr.Button("Score Essay")
-
     submit_btn.click(
         fn=score_essay,
         inputs=essay_input,
@@ -96,7 +129,7 @@ with gr.Blocks() as demo:
     )
 
     gr.Markdown(
-        "⚠️ **Note:** This is an AI-based estimator, not an official IELTS examiner score."
+        "⚠️ Note: AI-based estimator; not an official IELTS score."
     )
 
 # -------------------------------