Update app.py

app.py

@@ -1,77 +1,112 @@
-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": "#E6E6FA"},        
-    "sadness": {"emoji": "😢", "color": "#3498DB"},    
-    "anger": {"emoji": "😡", "color": "#FFDAB9"},      
-    "fear": {"emoji": "😨", "color": "#FFFACD"},       
-    "surprise": {"emoji": "😲", "color": "#98FB98"},   
-    "disgust": {"emoji": "🤢", "color": "#FFB6C1"},          
-    "neutral": {"emoji": "😐", "color": "#D3D3D3"}    
-}
-
-# 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("innomatics-footer-logo.webp", use_container_width=True,width=300) 
-
-st.markdown("<h1 style='text-align: center; color: #7D3C98;'>Emotion Detection using ML</h1>", unsafe_allow_html=True)
-st.markdown("<h3 style='text-align: left; color: #3498DB;'>📝 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!")
+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)
+
+# Emotion Mapping (Emojis & Colors)
+emotion_styles = {
+    "joy": {"emoji": "😃", "color": "#E6E6FA"},
+    "sadness": {"emoji": "😢", "color": "#3498DB"},
+    "anger": {"emoji": "😡", "color": "#FFDAB9"},
+    "fear": {"emoji": "😨", "color": "#FFFACD"},
+    "surprise": {"emoji": "😲", "color": "#98FB98"},
+    "disgust": {"emoji": "🤢", "color": "#FFB6C1"},
+    "neutral": {"emoji": "😐", "color": "#D3D3D3"}
+}
+
+# Configure Streamlit Page
+st.set_page_config(page_title="Emotion Detection", layout="centered")
+
+# Custom CSS for Background and Styling
+st.markdown(
+    """
+    <style>
+        body { background-color: black; color: white; }
+        .result-box {
+            text-align: center;
+            padding: 15px;
+            border-radius: 10px;
+            font-size: 22px;
+            font-weight: bold;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Header Section
+st.image("innomatics-footer-logo.webp", use_container_width=True)  # Replace with your image file
+st.markdown("<h1 style='text-align: center; color: blue;'>🔍 Emotion Detection 😊</h1>", unsafe_allow_html=True)
+
+# Business Context
+st.markdown("<h2 style='color: orange;'> 🎯 Business Context</h2>",unsafe_allow_html=True)
+
+st.markdown("<h3 style='color: red;'>📌 Business Problem</h3>", unsafe_allow_html=True)
+st.markdown("""
+Businesses struggle to understand customer emotions in real-time. Traditional feedback methods, such as surveys and reviews, fail to capture spontaneous emotional responses, leading to:
+
+- **Missed opportunities** for improving customer experience.
+- **Delayed insights** into customer satisfaction.
+- **Inability to personalize interactions** based on real emotions.
+
+An effective emotion detection system can help businesses analyze customer sentiments instantly, enabling proactive engagement and improved decision-making.
+""", unsafe_allow_html=True)
+
+st.markdown("<h3 style='color: green;'>🎯 Business Objective</h3>", unsafe_allow_html=True)
+st.markdown("""
+The primary goal of this Emotion Detection System is to enhance customer experience by identifying emotions in real-time from text-based interactions.
+
+##### **Key Objectives:**
+- ✅ **Real-time Emotion Analysis** – Detect emotions from customer messages, emails, and social media interactions.
+- ✅ **Improved Customer Satisfaction** – Address negative sentiments promptly to enhance brand loyalty.
+- ✅ **Personalized Engagement** – Tailor responses based on detected emotions for a better user experience.
+- ✅ **Data-Driven Decisions** – Provide insights for optimizing services, marketing strategies, and customer interactions.
+- ✅ **Operational Efficiency** – Automate sentiment analysis, reducing manual effort and response time.
+""", unsafe_allow_html=True)
+
+st.markdown("<h3 style='color: blue;'>⚖️ Business Constraints</h3>", unsafe_allow_html=True)
+st.markdown("""
+The system must meet the following business and technical constraints:
+
+- 1️⃣ **Data Privacy & Compliance** – Must adhere to regulations like **GDPR** and **CCPA** to ensure user data protection.
+- 2️⃣ **Real-time Processing** – The model should analyze and respond to emotions instantly, without significant delays.
+- 3️⃣ **System Integration** – Should seamlessly integrate with **chatbots, CRMs, call centers, and social media platforms**.
+- 4️⃣ **Accuracy & Reliability** – High **precision with minimal false positives** to avoid misinterpretations.
+- 5️⃣ **Scalability** – Should efficiently handle **large-scale interactions** across multiple customer touchpoints.
+- 6️⃣ **Cost-effectiveness** – Must be financially viable while delivering measurable ROI.
+- 7️⃣ **Multi-language & Multi-model Support** – Capable of detecting emotions across **various languages** and communication channels **(text, voice, images)**.
+""", unsafe_allow_html=True)
+
+# User Input Section
+st.markdown("<h2 style='color: purple;'> 📝 Enter Your Text Below</h2>",unsafe_allow_html=True)
+user_text = st.text_input("", placeholder="Type your text here...")
+
+# Emotion Prediction
+if st.button("Predict Emotion"):
+    if user_text:
+        inputs = tokenizer(user_text, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # Get predicted emotion
+        scores = outputs.logits[0]
+        predicted_label_id = torch.argmax(scores).item()
+        predicted_emotion = model.config.id2label[predicted_label_id].lower()
+
+        # Display Results
+        emotion_data = emotion_styles.get(predicted_emotion, {"emoji": "😐", "color": "#95A5A6"})
+        st.markdown(
+            f"""
+            <div class="result-box" style="background-color: {emotion_data['color']}; color: black;">
+                Detected Emotion: <b>{predicted_emotion.capitalize()} {emotion_data['emoji']}</b>
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+    else:
+        st.warning("Please enter some text!")