Update app.py

app.py

@@ -1,13 +1,16 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 from torch.nn.functional import sigmoid
 import torch
 
-# Load multi-label GoEmotions model (Active)
+# 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 classification pipeline
+image_emotion_pipeline = pipeline("image-classification", model="nateraw/ferplus-emo-resnet34")
+
 # Emotion label to icon mapping (subset)
 emotion_icons = {
     "admiration": "😍",
@@ -40,7 +43,7 @@ emotion_icons = {
     "neutral": "😐"
 }
 
-# Prediction function with adjustable threshold
+# Analyze text emotion
 def get_emotions(text, threshold):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
@@ -52,6 +55,20 @@ def get_emotions(text, threshold):
 
     return ", ".join(icons) if icons else "No strong emotion detected."
 
+# Analyze image emotion
+def analyze_image_emotion(image):
+    if image is None:
+        return "No image provided."
+    results = image_emotion_pipeline(image)
+    top = results[0]
+    return f"{top['label']} ({top['score']:.2f})"
+
+# Combined analysis
+def analyze_combined(text, threshold, image):
+    text_result = get_emotions(text, threshold)
+    image_result = analyze_image_emotion(image)
+    return text_result, image_result
+
 # Gradio UI
 custom_css = """
 body {
@@ -84,14 +101,18 @@ body {
 """
 
 demo = gr.Interface(
-    fn=get_emotions,
+    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.Slider(minimum=0.1, maximum=0.9, value=0.3, step=0.05, label="Threshold"),
+        gr.Image(type="filepath", label="Upload Face Photo")
+    ],
+    outputs=[
+        gr.Textbox(label="Detected Text Emotions", elem_classes=["output-textbox"]),
+        gr.Textbox(label="Detected Photo Emotion", elem_classes=["output-textbox"])
     ],
-    outputs=gr.Textbox(label="Detected Emotions", elem_classes=["output-textbox"]),
-    title="🥰 Multi-Label Emotion Detector",
-    description="Enter a sentence or paragraph to detect multiple emotions present in the text. Adjust the threshold to be more or less sensitive.",
+    title="🥰 Multi-Modal Emotion Detector",
+    description="Analyze emotion from both text and a facial photo. Adjust the threshold for text emotion sensitivity.",
     theme="default",
     css=custom_css
 )