Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import pandas as pd
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoImageProcessor, AutoModelForImageClassification
 from torch.nn.functional import sigmoid
 import torch
@@ -43,6 +44,21 @@ def analyze_combined(text, threshold, image):
     final_label = text_label if img_label is None else img_label
     card = islamic_advice.get(final_label, islamic_advice["neutral"])
     return card
+    return f"<h2 style='font-size:2rem;text-align:center;'>{final_label.capitalize()}</h2><p style='text-align:center;font-size:1.2rem;'>{advice}</p>"
+
+def analyze_batch_csv(file):
+    df = pd.read_csv(file.name)
+    results = []
+    for text in df['text']:
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        probs = sigmoid(logits)[0]
+        idx = torch.argmax(probs).item()
+        label = model.config.id2label[idx].lower()
+        advice = label.capitalize()
+        results.append({"text": text, "emotion": label, "advice": advice})
+    return pd.DataFrame(results)
 
 custom_css = """
 body {
@@ -62,80 +78,23 @@ body {
 .gr-button:hover {
   background-color: #2563eb !important;
 }
-.notion-card {
-  background: #ffffff;
-  border-radius: 12px;
-  border: 1px solid #e5e7eb;
-  padding: 16px;
-  margin-top: 12px;
-  box-shadow: 0 6px 20px rgba(0,0,0,0.05);
-  max-width: 600px;
-  margin-left: auto;
-  margin-right: auto;
-  animation: fadeInUp 0.6s ease-out;
-}
-.notion-card h3 {
-  margin-top: 0;
-  color: #111827;
-  font-size: 1.25rem;
-  margin-bottom: 8px;
-  font-weight: 600;
-}
-.notion-card p {
-  font-size: 1rem;
-  color: #374151;
-  margin: 0;
-}
-.emoji {
-  text-align: center;
-  font-size: 3rem;
-  margin: 0;
-  padding: 0;
-  animation: popIn 0.5s ease;
-}
-.arabic {
-  font-size: 1.3rem;
-  direction: rtl;
-  display: block;
-  margin-top: 4px;
-  margin-bottom: 4px;
-  text-align: right;
-  font-family: 'Scheherazade', serif;
-}
-@keyframes fadeInUp {
-  0% {
-    opacity: 0;
-    transform: translateY(20px);
-  }
-  100% {
-    opacity: 1;
-    transform: translateY(0);
-  }
-}
-@keyframes popIn {
-  0% {
-    transform: scale(0.5);
-    opacity: 0;
-  }
-  100% {
-    transform: scale(1);
-    opacity: 1;
-  }
-}
 """
 
-demo = gr.Interface(
-    fn=analyze_combined,
-    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.HTML(label="Detected Emotion & Islamic Insight"),
-    title="🧠 EmotionLens: Understand & Reflect Emotionally",
-    description="Analyze how you feel through text and facial expression, and receive inspirational Islamic wisdom in a beautifully styled reflection.",
-    theme="default",
-    css=custom_css
-)
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("# 🧠 EmotionLens")
+    gr.Markdown("Analyze your text and optionally a facial photo. Receive emotional insight and reflective Islamic advice.")
+
+    with gr.Tab("Single Input"):
+        text_input = gr.Textbox(lines=4, label="Text Input")
+        threshold_slider = gr.Slider(0.1, 0.9, value=0.3, step=0.05, label="Threshold")
+        image_input = gr.Image(type="pil", label="Upload Face Photo (optional)")
+        btn = gr.Button("Analyze Emotion")
+        result = gr.HTML()
+        btn.click(fn=analyze_combined, inputs=[text_input, threshold_slider, image_input], outputs=result)
+
+    with gr.Tab("Batch Analysis"):
+        file_input = gr.File(file_types=[".csv"], label="Upload CSV with 'text' column")
+        table_output = gr.Dataframe()
+        file_input.change(fn=analyze_batch_csv, inputs=file_input, outputs=table_output)
 
-demo.launch()
+demo.launch()