e-motion project using gradio

README.md

@@ -1,12 +1,13 @@
 ---
 title: E Motion
-emoji: 💻
-colorFrom: indigo
-colorTo: yellow
+emoji: 🏃
+colorFrom: red
+colorTo: blue
 sdk: gradio
 sdk_version: 4.13.0
-app_file: app.py
+app_file: /src/app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,12 @@
+# Main dependencies
+
+gradio==4.2.0
+transformers==4.35.0
+scipy==1.11.3
+numpy==1.26.1
+torch==1.12.1
+
+# Development dependencies
+pylint==3.0.2
+isort==5.12.0
+black==23.11.0

src/app.py

@@ -0,0 +1,6 @@
+"""Main module to run the Gradio interface for sentiment analysis."""
+
+from gradio_interface import demo
+
+if __name__ == "__main__":
+    demo.launch(inbrowser=True)

src/gradio_interface.py

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+"""Module for setting up the Gradio interface for sentiment analysis."""
+import pathlib
+
+import gradio as gr
+from twitter_roberta import predict_sentiment
+
+theme = gr.themes.Base(
+    primary_hue="indigo",
+    font=[
+        gr.themes.GoogleFont("MD Mono"),
+        "ui-sans-serif",
+        "system-ui",
+        "sans-serif",
+    ],
+    font_mono=[
+        gr.themes.GoogleFont("Lato"),
+        "ui-monospace",
+        "Consolas",
+        "monospace",
+    ],
+).set(
+    body_background_fill_dark="linear-gradient(45deg, rgba(23,19,57,1) 0%, rgba(6,2,13,1) 100%);",
+    body_background_fill="linear-gradient(45deg, rgba(184,201,255,1) 0%, rgba(114,52,224,1) 100%);",
+    body_text_color="*primary_900",
+    body_text_color_subdued="*neutral_950",
+    body_text_color_subdued_dark="*primary_300",
+    button_secondary_background_fill="*primary_300",
+    button_secondary_background_fill_dark="*primary_600",
+    button_secondary_background_fill_hover="*primary_100",
+    button_secondary_background_fill_hover_dark="*primary_400",
+    button_secondary_text_color="*neutral_950",
+)
+
+
+# Set up the Gradio interface for the application.
+with gr.Blocks(theme=theme, title="🙂 E-motion 🙃") as demo:
+    with gr.Row():
+        with gr.Column(scale=3):
+            pass
+        with gr.Column(scale=1):
+            gr.Image(
+                "assets/e-motion_logo_17.svg",  # Convert the Path object to a string
+                height=145,
+                show_download_button=False,
+                container=False,
+                interactive=False,
+            )
+        with gr.Column(scale=3):
+            pass
+    with gr.Row():
+        with gr.Column():
+            box = gr.Textbox(
+                placeholder="Type something to check sentiment! 🤔",
+                label="🚀 Give it a go!",
+                info="We are classifying meaning behind your text.",
+                max_lines=10,
+            )
+            gr.ClearButton(box)
+        with gr.Column():
+            outputs = gr.Label(
+                value="😴 nothing to show yet...",
+                num_top_classes=3,
+                label="results",
+            )
+            btn = gr.Button("Classify")
+            # pylint: disable=no-member
+            btn.click(predict_sentiment, inputs=[box], outputs=[outputs])
+            # pylint: enable=no-member
+    gr.Markdown("Choose some ideas from below and see what it brings you back:")
+    gr.Examples(
+        [
+            "I love you.",
+            "Do you wanna go eat something with us?",
+            "Go away!",
+            "Amazing work, I see some improvements to make though.",
+            "Are you out of your mind!?",
+            "I can't shake off this constant feeling of worry and fear. It's affecting my daily life, and I don't know how to cope.",
+            "I can't help but feel like I'm not good enough. No matter what I do, it feels like I'm always falling short.",
+            "I'm so tired of feeling like this. I just want to feel normal again.",
+            "I feel like I'm going crazy. I can't stop thinking about all the things that could go wrong.",
+        ],
+        inputs=[box],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

src/twitter_roberta.py

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+"""Module for handling Twitter RoBERTa model loading and sentiment prediction."""
+
+import numpy as np
+from scipy.special import softmax
+from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
+
+# Load tokenizer and model
+MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+
+
+def preprocess(text: str) -> str:
+    """Preprocess the input text by replacing user mentions and URLs."""
+    return " ".join(
+        [
+            "@user" if t.startswith("@") else "http" if t.startswith("http") else t
+            for t in text.split()
+        ],
+    )
+
+
+def predict_sentiment(text: str) -> dict:
+    """Predict the sentiment of the given text using the RoBERTa model."""
+    text = preprocess(text)
+    encoded_input = tokenizer(text, return_tensors="pt")
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    ranking = np.argsort(scores)[::-1]
+    return {config.id2label[rank]: np.round(float(scores[rank]), 4) for rank in ranking}