Upload 5 files

app.py

@@ -0,0 +1,150 @@
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+# Streamlit UI
+st.set_page_config(page_title="Emotion Detection", layout="centered")
+# 🎯 Add Image at the Top
+st.image("inno.jpg", use_container_width=True,width=300) 
+
+#1. Business and Data Understanding
+st.header(" 1. Business and Data Understanding")
+st.write("""
+         Understanding human emotions is critical for businesses that interact with customers, employees, or users through text, speech, or images.
+         Subjectivity of Emotions: Emotions can be complex, overlapping, and vary based on individual perceptions.
+* Data Limitations: Labeled emotional datasets are often limited or imbalanced.
+* Real-time Processing Needs: Businesses require fast, scalable solutions for handling large volumes of data.
+* Multimodal Complexity: Emotions can be expressed through text, speech, or facial expressions, requiring different ML models.
+* Privacy & Ethical Concerns: Emotion analysis can raise ethical and legal issues related to data privacy.""")
+
+# b) Business Objective
+st.subheader(" b) Business Objective")
+st.write("""
+The primary business objective of emotion detection is to understand, analyze, and
+          respond to human emotions in real time to improve customer experience,
+          employee engagement, marketing strategies, and decision-making. 
+Key Business Objectives:
+1 Improve Customer Experience & Satisfaction:
+- Detect customer emotions in support chats, emails, and voice calls.
+- Provide personalized responses and proactive service to reduce customer frustration.
+- Optimize chatbots and virtual assistants to improve engagement.
+
+2 Enhance Brand Reputation & Market Intelligence:
+- Analyze social media sentiments and product reviews to measure public perception.
+- Identify negative feedback early to prevent PR crises.
+- Improve product and service offerings based on emotional insights.
+
+3 Increase Employee Engagement & Workplace Well-being:
+-  Analyze employee sentiment in feedback surveys, emails, and communication tools.
+- Detect burnout, dissatisfaction, or stress levels in employees.
+- Improve HR policies and work culture based on emotion analytics.
+
+4 Optimize Marketing & Advertising Strategies:
+- Measure emotional reactions to ads, videos, and brand campaigns.
+- Personalize marketing content based on user emotions.
+- Improve product recommendations and customer targeting.
+
+5 Support Mental Health & Well-being:
+- Detect signs of stress, anxiety, or depression in conversations.
+-  Provide AI-driven emotional support through chatbots and virtual therapy.
+- Assist psychologists and therapists in tracking patient emotions.
+
+6 Enable Real-Time Decision Making:
+- Use emotion-based insights to make faster, data-driven business decisions.
+-  Improve customer retention by addressing negative sentiments proactively.
+- Automate sentiment monitoring for large-scale data analysis.""")
+
+# c) Business Constraints
+
+st.subheader(" c) Business Constraints")
+st.write("""
+- *Data Privacy & Security*
+- *Language & Emoji Variations*
+- *Accuracy & Context Awareness*
+- *scalability*
+- *Real-Time Processing*
+""")
+
+# d) Data Understanding
+st.subheader(" d) Data Understanding")
+st.write("""
+- *Dataset Size:* 422,746 text samples.
+- *Data Types:*  Text data, labeled emotions, unstructured text.
+- *Data Features:*
+ *Raw Text:* Actual emotional expressions.
+ *Emojis & Symbols:* Indicators of sentiment.
+""")
+
+
+# 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"}    
+}
+
+
+# 🎨 Change Background Color
+st.markdown(
+    """
+    <style>
+    body {
+        background-color:#AFEEEE; /* Light Grayish Blue */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+
+
+st.markdown("<h1 style='text-align: center; color: #7B3B98;'>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!")

data.csv

@@ -0,0 +1,7 @@
+customer_id,name,contact,email,age,location,subscription_type,payment_plan,num_subscription_pauses,payment_method,customer_service_inquiries,signup_date,weekly_hours,average_session_length,song_skip_rate,weekly_songs_played,weekly_unique_songs,num_favorite_artists,num_platform_friends,num_playlists_created,num_shared_playlists,notifications_clicked
+121530,aadhi,8977893494,aadhi01516@gmail.com,38,New Jersey,Family,Monthly,3,Paypal,Medium,-2900,38.07676034,59.23617873,0.830366768,69,57,24,18,76,32,3
+119139,moon,9600814004,prahalya128@gmail.com,36,Idaho,Free,Monthly,2,Debit Card,Low,-2603,14.98920398,10.72029557,0.659665888,352,293,35,25,19,10,35
+121530,aadhi,8977893494,k,38,New Jersey,Family,Monthly,3,Paypal,Medium,-2900,38.07676034,59.23617873,0.830366768,69,57,24,18,76,32,3
+119139,moon,9600814004,k,36,Idaho,Free,Monthly,2,Debit Card,Low,-2603,14.98920398,10.72029557,0.659665888,352,293,35,25,19,10,35
+121530,aadhi,8977893494,k,38,New Jersey,Family,Monthly,3,Paypal,Medium,-2900,38.07676034,59.23617873,0.830366768,69,57,24,18,76,32,3
+119139,moon,9600814004,k,36,Idaho,Free,Monthly,2,Debit Card,Low,-2603,14.98920398,10.72029557,0.659665888,352,293,35,25,19,10,35

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57b403cfd4304d36b9b2eb190de505084ae31c67430fa83c779aa7ff03622767
+size 2069

pipe.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa81099c9687a11a62b5dbc7aa47d9dfb316ce4861df254d9a1afdab6aa9bb6b
+size 3082

requirements.txt

@@ -0,0 +1,6 @@
+pandas
+numpy
+nltk
+scikit-learn
+streamlit
+joblib