Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+import gradio as gr
+
+ 
+tokenizer_sentence_analysis = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
+model_sentence_analysis = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
+
+tokenizer_review_feedback_sentiment = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
+model_review_feedback_sentiment = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
+
+classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+
+def sentence_sentiment_model(text, tokenizer, model):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        result = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+        logits = result.logits.detach()
+        probs = torch.softmax(logits, dim=1)
+    pos_prob = probs[0][2].item()
+    neu_prob = probs[0][1].item()  
+    neg_prob = probs[0][0].item() 
+    return {'Positive': [round(float(pos_prob), 2)],"Neutural":[round(float(neu_prob), 2)], 'Negative': [round(float(neg_prob), 2)]}
+
+def review_feedback_sentiment(text, tokenizer, model):
+    inputs = tokenizer.encode_plus(text, padding='max_length', max_length=512, return_tensors="pt")
+    with torch.no_grad():
+        result = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+        logits = result.logits.detach()
+        probs = torch.softmax(logits, dim=1).detach().numpy()[0]
+        categories = ['Terrible', 'Poor', 'Average', 'Good', 'Excellent']
+        output_dict = {}
+        for i in range(len(categories)):
+            output_dict[categories[i]] = [round(float(probs[i]), 2)]
+    return output_dict
+
+
+
+def emotion_sentiment(text):
+    results = classifier(text, padding='max_length', max_length=512)
+    return {label['label']: [label['score']] for label in results[0]}
+ 
+
+
+def sentence_analysis(text):
+    result = sentence_sentiment_model(text,tokenizer_sentence_analysis,model_sentence_analysis)
+    return result
+def emotion(text):
+    result = emotion_sentiment(text)
+    return result
+def review_feed_back(text):
+    result = review_feedback_sentiment(text,tokenizer_review_feedback_sentiment,model_review_feedback_sentiment)
+    return result
+
+def selection_model(model,text):
+    if text == "":
+        return "No Text Input"
+    if model=="Emotion Analysis":
+        return emotion(text)
+    if model == "Review Feedback Analysis":
+        return review_feed_back(text)
+    if model == "Sentence Analysis":
+        return sentence_analysis(text)
+    return "Please select model"
+
+
+paragraph = """
+I woke up this morning feeling refreshed and excited for the day ahead. 
+""" 
+
+with gr.Blocks(title="Sentiment",css="footer {visibility: hidden}") as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Emotion, ReviewFeedback, Sentence Analysis")
+            with gr.Row():           
+                with gr.Column():
+                    drop_down_name = gr.Dropdown(choices=["Emotion Analysis", "Review Feedback Analysis", "Sentence Analysis"],label="Model")
+                    inputs = gr.TextArea(label="sentence",value=paragraph,interactive=True)
+                    btn = gr.Button(value="RUN")
+                with gr.Column():
+                    output = gr.Label(label="output") 
+                btn.click(fn=selection_model,inputs=[drop_down_name,inputs],outputs=[output])
+demo.launch() 
+
+