Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import pipeline
+
+model_path = "trnt/twitter_emotions"
+is_gpu = False
+device = torch.device('cuda') if is_gpu else torch.device('cpu')
+print(device)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.to(device)
+model.eval()
+print("Model was loaded")
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, device=is_gpu-1)
+emotions = {'LABEL_0': 'sadness', 'LABEL_1': 'joy', 'LABEL_2': 'love', 'LABEL_3': 'anger', 'LABEL_4': 'fear',
+            'LABEL_5': 'surprise'}
+examples = ["I love you!", "I hate you!"]
+
+
+def predict(twitter):
+    pred = classifier(twitter, return_all_scores=True)[0]
+    res = {"Sadness": pred[0]["score"],
+           "Joy": pred[1]["score"],
+           "Love": pred[2]["score"],
+           "Anger": pred[3]["score"],
+           "Fear": pred[4]["score"],
+           "Surprise": pred[5]["score"]}
+    # "This tweet is %s with probability=%.2f" % (emotions[pred['label']], 100 * pred['score']) + "%"
+    return res
+
+
+if __name__ == '__main__':
+    interFace = gr.Interface(fn=predict,
+                             inputs=gr.inputs.Textbox(placeholder="Enter a tweet here", label="Tweet content", lines=5),
+                             outputs=gr.outputs.Label(num_top_classes=6, label="Emotions of this tweet is "),
+                             verbose=True,
+                             examples=examples,
+                             title="Emotions of English tweet",
+                             description="",
+                             theme="grass")
+    interFace.launch()