| import gradio as gr | |
| from transformers import pipeline | |
| classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True) | |
| def detect_emotions(emotion_input): | |
| prediction = classifier(emotion_input,) | |
| output = {} | |
| for emotion in prediction[0]: | |
| output[emotion["label"]] = emotion["score"] | |
| return output | |
| examples = [["I am excited to announce that I have been promoted"], ["Sorry for the late reply"]] | |
| css = """ | |
| footer {display:none !important} | |
| .output-markdown{display:none !important} | |
| .gr-button-primary { | |
| z-index: 14; | |
| height: 43px; | |
| width: 130px; | |
| left: 0px; | |
| top: 0px; | |
| padding: 0px; | |
| cursor: pointer !important; | |
| background: none rgb(17, 20, 45) !important; | |
| border: none !important; | |
| text-align: center !important; | |
| font-family: Poppins !important; | |
| font-size: 14px !important; | |
| font-weight: 500 !important; | |
| color: rgb(255, 255, 255) !important; | |
| line-height: 1 !important; | |
| border-radius: 12px !important; | |
| transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; | |
| box-shadow: none !important; | |
| } | |
| .gr-button-primary:hover{ | |
| z-index: 14; | |
| height: 43px; | |
| width: 130px; | |
| left: 0px; | |
| top: 0px; | |
| padding: 0px; | |
| cursor: pointer !important; | |
| background: none rgb(66, 133, 244) !important; | |
| border: none !important; | |
| text-align: center !important; | |
| font-family: Poppins !important; | |
| font-size: 14px !important; | |
| font-weight: 500 !important; | |
| color: rgb(255, 255, 255) !important; | |
| line-height: 1 !important; | |
| border-radius: 12px !important; | |
| transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; | |
| box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important; | |
| } | |
| .hover\:bg-orange-50:hover { | |
| --tw-bg-opacity: 1 !important; | |
| background-color: rgb(229,225,255) !important; | |
| } | |
| .to-orange-200 { | |
| --tw-gradient-to: rgb(37 56 133 / 37%) !important; | |
| } | |
| .from-orange-400 { | |
| --tw-gradient-from: rgb(17, 20, 45) !important; | |
| --tw-gradient-to: rgb(255 150 51 / 0); | |
| --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important; | |
| } | |
| .group-hover\:from-orange-500{ | |
| --tw-gradient-from:rgb(17, 20, 45) !important; | |
| --tw-gradient-to: rgb(37 56 133 / 37%); | |
| --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important; | |
| } | |
| .group:hover .group-hover\:text-orange-500{ | |
| --tw-text-opacity: 1 !important; | |
| color:rgb(37 56 133 / var(--tw-text-opacity)) !important; | |
| } | |
| """ | |
| demo = gr.Interface(fn=detect_emotions, inputs=gr.Textbox(placeholder="Enter text here", label="Input"), outputs=gr.Label(label="Emotion"), title="Emotion Detector | Data Science Dojo", examples=examples, css=css) | |
| demo.launch() |