Update app.py

app.py

@@ -1,3 +1,100 @@
 import gradio as gr
 
-gr.load("models/Prasadrao/xlm-roberta-large-go-emotions-v3").launch()
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Prasadrao/xlm-roberta-large-go-emotions-v3")
+model = AutoModelForSequenceClassification.from_pretrained("Prasadrao/xlm-roberta-large-go-emotions-v3")
+
+emotion_labels = ["admiration", "amusement", "anger", "annoyance", "approval",
+                  "caring", "confusion", "curiosity", "desire", "disappointment",
+                  "disapproval", "disgust", "embarrassment", "excitement",
+                  "fear", "gratitude", "grief", "joy", "love", "nervousness",
+                  "optimism", "pride", "realization", "relief", "remorse",
+                  "sadness", "surprise", "neutral"]
+
+def get_sentiment_emoji(sentiment):
+    # Define the emojis corresponding to each sentiment
+    emoji_mapping = {
+        "disappointment": "😞",
+        "sadness": "😢",
+        "annoyance": "😠",
+        "neutral": "😐",
+        "disapproval": "👎",
+        "realization": "😮",
+        "nervousness": "😬",
+        "approval": "👍",
+        "joy": "😄",
+        "anger": "😡",
+        "embarrassment": "😳",
+        "caring": "🤗",
+        "remorse": "😔",
+        "disgust": "🤢",
+        "grief": "😥",
+        "confusion": "😕",
+        "relief": "😌",
+        "desire": "😍",
+        "admiration": "😌",
+        "optimism": "😊",
+        "fear": "😨",
+        "love": "❤️",
+        "excitement": "🎉",
+        "curiosity": "🤔",
+        "amusement": "😄",
+        "surprise": "😲",
+        "gratitude": "🙏",
+        "pride": "🦁"
+    }
+    return emoji_mapping.get(sentiment, "")
+
+import torch
+import torch.nn.functional as F
+
+def predict_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax().item()
+    predicted_emotion = emotion_labels[predicted_class]
+    emoji = get_sentiment_emoji(predicted_emotion)
+    predicted_emotion = f" {predicted_emotion} {emoji}"
+
+    # Calculate softmax to get the probabilities
+    probabilities = F.softmax(logits, dim=1)
+    # Extract the score for the predicted class
+    predicted_score = round(probabilities[0, predicted_class].item()*100)
+    predicted_score = f"{predicted_score}%"
+
+    return predicted_emotion, predicted_score
+
+import torch
+import os
+import gradio as gr
+import random
+
+#server_port = random.randint(1000, 9000)
+
+
+title = "Emotion Detective: Analyzing Textual Sentiments"
+description = "Explore the power of sentiment analysis with our Emotion Detective! Simply input a sentence or text, and let our model predict the underlying emotion."
+#article = "Sentiment Analysis, also known as opinion mining, is a branch of Natural Language Processing (NLP) that involves determining the emotional tone behind a piece of text"
+examples = [["I feel ecstatic about winning the competition."],
+            ["The news of her promotion made me feel proud and happy."],
+            ["He felt devastated after hearing about the loss of his pet."],
+            ["It's like I went through a rollercoaster of feelings!"],
+            ["The memory of his actions haunted him, filling him with a profound sense of sorrow."],
+            ["Despite the challenges ahead, he couldn't shake the feeling that good things were on the horizon"],
+            ["The repugnant smell wafting from the trash bin made her nose crinkle in discomfort"],
+            ["As he looked at his team's success, a feeling of triumph and contentment filled his heart."]]
+
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=3,placeholder="Enter your text here.....",label="Text"),
+    outputs=[gr.Textbox(label="Emotion"), gr.Textbox(label="Score")],
+    title=title,
+    description=description,
+    flagging_options = ["True","False","Not Sure"],
+    examples = examples,
+    
+)
+# Launch the Gradio interface
+demo.launch(share=True)