Remove BLEU

app.py

@@ -2,10 +2,7 @@ import gradio as gr
 from PIL import Image
 import torch
 from transformers import VisionEncoderDecoderModel, AutoTokenizer, ViTFeatureExtractor, AutoImageProcessor, AutoModelForImageClassification
-from nltk.translate.bleu_score import sentence_bleu
 import warnings
-import nltk
-nltk.download('punkt')
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
@@ -15,6 +12,9 @@ caption_model = VisionEncoderDecoderModel.from_pretrained("bombshelll/ViT_BioMed
 tokenizer = AutoTokenizer.from_pretrained("bombshelll/ViT_BioMedBert_Captioning_ROCO")
 feature_extractor = ViTFeatureExtractor.from_pretrained("bombshelll/ViT_BioMedBert_Captioning_ROCO")
 
+with open("style.css") as f:
+    custom_css = f.read()
+
 def load_classifier(model_id):
     processor = AutoImageProcessor.from_pretrained(model_id)
     model = AutoModelForImageClassification.from_pretrained(model_id).to(device)
@@ -38,23 +38,6 @@ def classify_image(image):
             results[name] = label
     return results
 
-def preprocess_caption(text):
-    text = str(text).lower()
-    text = text.replace("magnetic resonance imaging", "mri")
-    text = text.replace("magnetic resonance image", "mri")
-    text = text.replace("computed tomography", "ct")
-    text = text.replace("t1-weighted", "t1")
-    text = text.replace("t1w1", "t1")
-    text = text.replace("t1w", "t1")
-    text = text.replace("t1ce", "t1")
-    text = text.replace("t2-weighted", "t2")
-    text = text.replace("t2w", "t2")
-    text = text.replace("t2/flair", "flair")
-    text = text.replace("tumour", "tumor")
-    text = text.replace("lesions", "lesion")
-    text = text.replace("-", " ")
-    return text.split()
-
 def generate_captions(image, keywords):
     pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
 
@@ -78,69 +61,42 @@ def generate_captions(image, keywords):
 
     return caption1, caption2
 
-def run_pipeline(image, actual_caption):
+def run_pipeline(image):
     classification = classify_image(image)
     keywords = list(classification.values())
     caption1, caption2 = generate_captions(image, keywords)
 
-    if actual_caption.strip():
-        ref = [preprocess_caption(actual_caption)]
-        hyp1 = preprocess_caption(caption1)
-        hyp2 = preprocess_caption(caption2)
-        score1 = sentence_bleu(ref, hyp1, smoothing_function=nltk.translate.bleu_score.SmoothingFunction().method1)
-        score2 = sentence_bleu(ref, hyp2, smoothing_function=nltk.translate.bleu_score.SmoothingFunction().method1)
-        bleu1 = f"{score1:.2f}"
-        bleu2 = f"{score2:.2f}"
-    else:
-        bleu1 = "-"
-        bleu2 = "-"
-
-    result_sections = {
-        "classification": (
-            f"Plane: {classification.get('plane')}\n"
-            f"Modality: {classification.get('modality')}\n"
-            f"Abnormality: {classification.get('abnormality')}\n"
-            + (f"Tumor Type: {classification.get('tumor_type')}" if "tumor_type" in classification else "")
-        ),
-        "caption1": caption1,
-        "caption2": caption2,
-        "bleu1": bleu1,
-        "bleu2": bleu2
-    }
-
-    return (
-        result_sections["classification"],
-        result_sections["caption1"],
-        result_sections["bleu1"],
-        result_sections["caption2"],
-        result_sections["bleu2"]
+    classification_text = (
+        f"Plane: {classification.get('plane')}\n"
+        f"Modality: {classification.get('modality')}\n"
+        f"Abnormality: {classification.get('abnormality')}\n"
+        + (f"Tumor Type: {classification.get('tumor_type')}" if "tumor_type" in classification else "")
     )
 
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="pink"), css="*{font-family:'Poppins', sans-serif;}") as demo:
+    return classification_text, caption1, caption2
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="pink"), css=custom_css) as demo:
     gr.Markdown(
         """
         <link href="https://fonts.googleapis.com/css2?family=Poppins&display=swap" rel="stylesheet">
         <h1 style='text-align: center;'>🧠 Brain Hierarchical Classification + Captioning</h1>
-        <p style='text-align: center;'>Upload an MRI/CT brain image. The system will classify the image (plane, modality, abnormality, tumor type) and generate two captions. Optionally, provide a ground truth caption to get BLEU scores.</p>
+        <p style='text-align: center;'>Upload an MRI/CT brain image. The system will classify the image (plane, modality, abnormality, tumor type) and generate two captions.</p>
         """,
         elem_id="title"
     )
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(type="pil", label="🖼️ Upload Brain MRI/CT")
-            actual_caption = gr.Textbox(label="💬 Ground Truth Caption (optional)")
             btn = gr.Button("🚀 Submit")
         with gr.Column():
             cls_box = gr.Textbox(label="📋 Classification Result", lines=4)
             cap1_box = gr.Textbox(label="📝 Caption without Keyword Integration", lines=4)
-            bleu1_box = gr.Textbox(label="📊 BLEU Score (No Keyword)", lines=1)
             cap2_box = gr.Textbox(label="🧠 Caption with Keyword Integration", lines=4)
-            bleu2_box = gr.Textbox(label="📈 BLEU Score (With Keyword)", lines=1)
 
     btn.click(
         fn=run_pipeline,
-        inputs=[image_input, actual_caption],
-        outputs=[cls_box, cap1_box, bleu1_box, cap2_box, bleu2_box]
+        inputs=[image_input],
+        outputs=[cls_box, cap1_box, cap2_box]
     )
 
 demo.launch()

style.css

@@ -0,0 +1,13 @@
+* {
+  font-family: 'Poppins', sans-serif;
+}
+
+.gr-column > div {
+  max-height: 600px;
+  overflow-y: auto;
+}
+
+body, html {
+  margin: 0;
+  padding: 0;
+}