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app.py

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+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load pre-trained emotion detection model
+model_name = "j-hartmann/emotion-english-distilroberta-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Get the actual emotion labels from the model
+emotion_labels = model.config.id2label
+
+# Emotion styles (emoji + colors)
+emotion_styles = {
+    "joy": {"emoji": "😃", "color": "#D3D3D3"},        
+    "sadness": {"emoji": "😢", "color": "#3498BB"},    
+    "anger": {"emoji": "😡", "color": "#FFDAA9"},      
+    "fear": {"emoji": "😨", "color": "#FFFAAD"},       
+    "surprise": {"emoji": "😲", "color": "#98CB98"},   
+    "disgust": {"emoji": "🤢", "color": "#FFBFC1"},          
+    "neutral": {"emoji": "😐", "color": "#E6E6FA"}    
+}
+
+# Streamlit UI
+st.set_page_config(page_title="Emotion Detection", layout="centered")
+
+# 🎨 Change Background Color
+st.markdown(
+    """
+    <style>
+    body {
+        background-color:#AFEEEE; /* Light Grayish Blue */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# 🎯 Add Image at the Top
+st.image("inno.jpg", use_container_width=True,width=300) 
+
+st.markdown("<h1 style='text-align: center; color: #7D3B98;'>Emotion Detection using ML</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: left; color: #3498BB;'>📝 Enter the text:</h3>", unsafe_allow_html=True)
+
+# User Input
+user_text = st.text_input("", placeholder="Type your text here...")
+
+if st.button("Submit"):
+    if user_text:
+        # Tokenize input text
+        inputs = tokenizer(user_text, return_tensors="pt")
+
+        # Get model predictions
+        with torch.no_grad():
+
+
+            
+            outputs = model(**inputs)
+
+        # Get the predicted emotion
+        scores = outputs.logits[0]
+        predicted_label_id = torch.argmax(scores).item()
+        predicted_emotion = emotion_labels[predicted_label_id].strip().lower()
+
+        # Get emoji & color
+        emotion_data = emotion_styles.get(predicted_emotion, {"emoji": "😐", "color": "#D3D3D3"})
+        emoji_display = emotion_data["emoji"]
+        text_color = emotion_data["color"]
+
+        # Display Results with Color
+        st.markdown(
+            f"""
+            <div style="text-align: center; padding: 10px; border-radius: 10px; background-color: {text_color}; color: black; font-size: 24px;">
+                <b>Detected Emotion:</b> {predicted_emotion.capitalize()} {emoji_display}
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+    else:
+        st.warning("Please enter some text!")

requirements.txt

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+pandas
+numpy
+nltk
+scikit-learn
+streamlit
+joblib