Update app.py

app.py

@@ -4,42 +4,83 @@ from torch.nn.functional import sigmoid
 import torch
 from PIL import Image
 
+# Load models
+text_model_name = "SamLowe/roberta-base-go_emotions"
+tokenizer = AutoTokenizer.from_pretrained(text_model_name)
+text_model = AutoModelForSequenceClassification.from_pretrained(text_model_name)
 
-
-# Load text emotion model
-model_name = "SamLowe/roberta-base-go_emotions"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Load image emotion model
 image_model_name = "Celal11/resnet-50-finetuned-FER2013-0.001"
 image_processor = AutoImageProcessor.from_pretrained(image_model_name)
 image_model = AutoModelForImageClassification.from_pretrained(image_model_name)
 
-# Analyze image emotion using processor and model
-def analyze_image_emotion(image):
-    if image is None:
-        return "No image provided."
-    inputs = image_processor(images=image, return_tensors="pt")
-
-    with torch.no_grad():
-        logits = image_model(**inputs).logits
-    probs = torch.nn.functional.softmax(logits, dim=1)[0]
-    pred_idx = torch.argmax(probs).item()
-    label = image_model.config.id2label[pred_idx]
-    score = probs[pred_idx].item()
-    return f"{label} ({score:.2f})"
-
-# Emotion label to icon mapping (subset)
-emotion_icons = {
-    inputs=[
-        gr.Textbox(lines=5, placeholder="Write a sentence or a full paragraph...", label="Your Text"),
-        gr.Slider(minimum=0.1, maximum=0.9, value=0.3, step=0.05, label="Threshold"),
-        gr.Image(type="pil", label="Upload Face Photo")
-    ],
-    outputs=[
-        gr.Textbox(label="Detected Text Emotions", elem_classes=["output-textbox"]),
-    css=custom_css
+# Analyze function
+def analyze(text, threshold, image):
+    result_html = ""
+
+    if text:
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+        with torch.no_grad():
+            logits = text_model(**inputs).logits
+        probs = sigmoid(logits)[0]
+        top = torch.topk(probs, k=3)
+        top_emotions = [f"<li>{text_model.config.id2label[i]} ({probs[i]:.2f})</li>" for i in top.indices]
+        result_html += f"<div class='notion-card fade-in slide-up'><h3>📝 Text Emotion</h3><ul>{''.join(top_emotions)}</ul></div>"
+
+    if image:
+        inputs_image = image_processor(images=image, return_tensors="pt")
+        with torch.no_grad():
+            logits_img = image_model(**inputs_image).logits
+        probs_img = torch.nn.functional.softmax(logits_img, dim=1)[0]
+        img_idx = torch.argmax(probs_img).item()
+        img_label = image_model.config.id2label[img_idx]
+        confidence = probs_img[img_idx].item()
+        result_html += f"<div class='notion-card fade-in slide-up'><h3>🖼️ Image Emotion</h3><p>{img_label} ({confidence:.2f})</p></div>"
+
+    return result_html or "<div class='notion-card fade-in'><p>No input provided.</p></div>"
+
+# CSS
+custom_css = """
+@keyframes fadeInPop {
+  0% { opacity: 0; transform: scale(0.95); }
+  100% { opacity: 1; transform: scale(1); }
+}
+.fade-in {
+  animation: fadeInPop 0.6s ease-out both;
 }
+.slide-up {
+  animation: slideInUp 0.6s ease-out both;
+}
+@keyframes slideInUp {
+  from { transform: translateY(20px); opacity: 0; }
+  to { transform: translateY(0); opacity: 1; }
+}
+.notion-card {
+  background: white;
+  border-radius: 12px;
+  border: 1px solid #e5e7eb;
+  padding: 16px;
+  margin: 16px auto;
+  box-shadow: 0 6px 20px rgba(0,0,0,0.05);
+  max-width: 600px;
+}
+body {
+  background: #f9fafb;
+  font-family: 'Inter', sans-serif;
+}
+"""
+
+# UI
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("# 🧠 EmotionLens")
+    gr.Markdown("Detect emotion from text and face image.")
+
+    with gr.Row():
+        text_input = gr.Textbox(label="Your Text", lines=3, placeholder="How do you feel?")
+        image_input = gr.Image(type="pil", label="Upload Face Photo")
+    threshold_slider = gr.Slider(0.1, 0.9, value=0.3, label="Threshold")
+    analyze_btn = gr.Button("Analyze")
+    output = gr.HTML()
+
+    analyze_btn.click(fn=analyze, inputs=[text_input, threshold_slider, image_input], outputs=output)
 
-demo.launch()
+demo.launch()