Update app.py

app.py

@@ -1,86 +1,87 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoImageProcessor, AutoModelForImageClassification
-from torch.nn.functional import sigmoid
-import torch
+from transformers import pipeline
 from PIL import Image
+import torch
+from torchvision import transforms
+from torchvision.models import resnet50
+import torch.nn.functional as F
 
-# 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 detection pipeline
+text_emotion_pipeline = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions", top_k=3)
 
-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)
+# Load image emotion detection model
+image_model = torch.hub.load('pytorch/vision', 'resnet50', pretrained=False)
+image_model.fc = torch.nn.Linear(image_model.fc.in_features, 7)
+image_model.load_state_dict(torch.hub.load_state_dict_from_url(
+    'https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.001/resolve/main/pytorch_model.bin',
+    map_location=torch.device('cpu')
+))
+image_model.eval()
 
-# Analyze function
-def analyze(text, threshold, image):
-    result_html = ""
+image_emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
 
-    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>"
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
 
-    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>"
+# Predefined reflections (simplified mapping)
+spiritual_reflections = {
+    "Sadness": "For indeed, with hardship [will be] ease. (Qur’an 94:6)",
+    "Joy": "Say, ‘In the bounty of Allah and in His mercy – in that let them rejoice.’ (Qur’an 10:58)",
+    "Fear": "And whoever fears Allah – He will make for him a way out. (Qur’an 65:2)",
+    "Anger": "Those who restrain anger and who pardon the people – Allah loves the doers of good. (Qur’an 3:134)",
+    "Surprise": "They plan, and Allah plans. Surely, Allah is the best of planners. (Qur’an 8:30)",
+    "Disgust": "Indeed, the most noble of you in the sight of Allah is the most righteous. (Qur’an 49:13)",
+    "Neutral": "Verily, in the remembrance of Allah do hearts find rest. (Qur’an 13:28)"
+}
 
-    return result_html or "<div class='notion-card fade-in'><p>No input provided.</p></div>"
+# Text input handler
+def analyze_text_emotion(text):
+    emotions = text_emotion_pipeline(text)
+    top_emotion = emotions[0]["label"]
+    reflection = spiritual_reflections.get(top_emotion, "Reflect with patience and prayer. (Qur’an 2:153)")
+    return top_emotion, reflection
 
-# 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;
-}
-"""
+# Image input handler
+def analyze_image_emotion(image):
+    img_tensor = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        logits = image_model(img_tensor)
+        probs = F.softmax(logits, dim=1)[0]
+    top_idx = torch.argmax(probs).item()
+    top_emotion = image_emotions[top_idx]
+    reflection = spiritual_reflections.get(top_emotion, "Reflect with patience and prayer. (Qur’an 2:153)")
+    return top_emotion, reflection
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 EmotionLens — AI-Powered Emotional Intelligence with Islamic Wisdom")
+    with gr.Tabs():
+        with gr.TabItem("Single Input"):
+            with gr.Row():
+                text_input = gr.Textbox(lines=2, placeholder="Enter text here...")
+                img_input = gr.Image(type="pil")
+
+            with gr.Row():
+                submit_btn = gr.Button("Submit")
 
-# UI
-with gr.Blocks(css=custom_css) as demo:
-    gr.Markdown("# 🧠 EmotionLens")
-    gr.Markdown("Detect emotion from text and face image.")
+            with gr.Column():
+                emotion_output = gr.Textbox(label="Emotion Detection")
+                reflection_output = gr.Textbox(label="Spiritual Reflection")
 
-    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()
+            def combined_handler(text, img):
+                if text:
+                    return analyze_text_emotion(text)
+                elif img:
+                    return analyze_image_emotion(img)
+                else:
+                    return "No input", "Please provide text or image."
 
-    analyze_btn.click(fn=analyze, inputs=[text_input, threshold_slider, image_input], outputs=output)
+            submit_btn.click(
+                fn=combined_handler,
+                inputs=[text_input, img_input],
+                outputs=[emotion_output, reflection_output]
+            )
 
 demo.launch()