Update app.py

app.py

@@ -7,10 +7,14 @@ from transformers import pipeline
 import gradio as gr
 from sklearn.cluster import KMeans
 from colorsys import rgb_to_hsv
+from huggingface_hub import InferenceClient  # Import InferenceClient for API calls
 
-# Emotion detection pipeline for text (if any text is included in assets)
+# Initialize the emotion detection pipeline for text (if any text is included in assets)
 emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
 
+# Initialize the Hugging Face API client with your Space model ID
+client = InferenceClient("nehapasricha94/LLaVA-image-analysis")
+
 # Function to analyze colors in an image
 def analyze_colors(image):
     try:
@@ -117,8 +121,8 @@ def analyze_patterns(image):
 def compute_overall_result(color_emotions, stress_levels, pattern_analysis, pattern_stress):
     try:
         # Assigning weightage to different factors
-        color_emotion_weight = 0.5  # 70% for color-based emotions and stress
-        pattern_weight = 0.5  # 30% for pattern analysis
+        color_emotion_weight = 0.5  # 50% for color-based emotions and stress
+        pattern_weight = 0.5  # 50% for pattern analysis
 
         # Determine the most common emotion from colors
         dominant_emotion = max(set(color_emotions), key=color_emotions.count)
@@ -153,6 +157,18 @@ def compute_overall_result(color_emotions, stress_levels, pattern_analysis, patt
         return f"Error computing overall result: {str(e)}"
 
 
+# Function to analyze emotions from a given text (if applicable)
+def analyze_emotion_from_text(text):
+    try:
+        # Using the local emotion classifier
+        emotion_scores = emotion_classifier(text)
+        dominant_emotion = max(emotion_scores, key=lambda x: x['score'])
+        return f"Detected emotion from text: {dominant_emotion['label']} with score: {dominant_emotion['score']:.2f}"
+    except Exception as e:
+        print(f"Error analyzing emotion from text: {e}")
+        return "Error analyzing text emotion"
+
+
 # Main function to process image and analyze emotional expression
 def analyze_emotion_from_image(image):
     try: