Update app.py

app.py

@@ -1,26 +1,71 @@
 import gradio as gr
-from textblob import TextBlob
-
-def analyze_sentiment(text):
-    blob = TextBlob(text)
-    sentiment = blob.sentiment.polarity
-    if sentiment > 0:
-        color = "green"
-    elif sentiment < 0:
-        color = "red"
-    else:
-        color = "yellow"
-    return color
-
-def sentiment_analysis(text):
-    color = analyze_sentiment(text)
-    # return "<h2 style='color:{}; text-align:center;'>{} Sentiment</h2>".format(color, label)
-    return "<h2><svg fill="#ff0000" width="213px" height="213px" viewBox="0 0 64 64" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"> <rect id="Icons" x="-640" y="-64" width="1280" height="800" style="fill:none;"></rect> <g id="Icons1" serif:id="Icons"> <g id="Strike"> </g> <g id="H1"> </g> <g id="H2"> </g> <g id="H3"> </g> <g id="list-ul"> </g> <g id="hamburger-1"> </g> <g id="hamburger-2"> </g> <g id="list-ol"> </g> <g id="list-task"> </g> <g id="trash"> </g> <g id="vertical-menu"> </g> <g id="horizontal-menu"> </g> <g id="sidebar-2"> </g> <g id="Pen"> </g> <g id="Pen1" serif:id="Pen"> </g> <g id="clock"> </g> <g id="external-link"> </g> <g id="hr"> </g> <g id="info"> </g> <g id="warning"> <path d="M32.427,7.987c2.183,0.124 4,1.165 5.096,3.281l17.936,36.208c1.739,3.66 -0.954,8.585 -5.373,8.656l-36.119,0c-4.022,-0.064 -7.322,-4.631 -5.352,-8.696l18.271,-36.207c0.342,-0.65 0.498,-0.838 0.793,-1.179c1.186,-1.375 2.483,-2.111 4.748,-2.063Zm-0.295,3.997c-0.687,0.034 -1.316,0.419 -1.659,1.017c-6.312,11.979 -12.397,24.081 -18.301,36.267c-0.546,1.225 0.391,2.797 1.762,2.863c12.06,0.195 24.125,0.195 36.185,0c1.325,-0.064 2.321,-1.584 1.769,-2.85c-5.793,-12.184 -11.765,-24.286 -17.966,-36.267c-0.366,-0.651 -0.903,-1.042 -1.79,-1.03Z" style="fill-rule:nonzero;"></path> <path d="M33.631,40.581l-3.348,0l-0.368,-16.449l4.1,0l-0.384,16.449Zm-3.828,5.03c0,-0.609 0.197,-1.113 0.592,-1.514c0.396,-0.4 0.935,-0.601 1.618,-0.601c0.684,0 1.223,0.201 1.618,0.601c0.395,0.401 0.593,0.905 0.593,1.514c0,0.587 -0.193,1.078 -0.577,1.473c-0.385,0.395 -0.929,0.593 -1.634,0.593c-0.705,0 -1.249,-0.198 -1.634,-0.593c-0.384,-0.395 -0.576,-0.886 -0.576,-1.473Z" style="fill-rule:nonzero;"></path> </g> <g id="plus-circle"> </g> <g id="minus-circle"> </g> <g id="vue"> </g> <g id="cog"> </g> <g id="logo"> </g> <g id="radio-check"> </g> <g id="eye-slash"> </g> <g id="eye"> </g> <g id="toggle-off"> </g> <g id="shredder"> </g> <g id="spinner--loading--dots-" serif:id="spinner [loading, dots]"> </g> <g id="react"> </g> <g id="check-selected"> </g> <g id="turn-off"> </g> <g id="code-block"> </g> <g id="user"> </g> <g id="coffee-bean"> </g> <g id="coffee-beans"> <g id="coffee-bean1" serif:id="coffee-bean"> </g> </g> <g id="coffee-bean-filled"> </g> <g id="coffee-beans-filled"> <g id="coffee-bean2" serif:id="coffee-bean"> </g> </g> <g id="clipboard"> </g> <g id="clipboard-paste"> </g> <g id="clipboard-copy"> </g> <g id="Layer1"> </g> </g> </g></svg></h2>"
-
-iface = gr.Interface(
-    fn=sentiment_analysis, 
-    inputs="text", 
-    outputs="html"
+import os
+import torch
+import spacy
+from spacy import displacy
+
+nlp = spacy.load("en_core_web_sm")
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+def get_hatespeech_score(text):
+    tokenizer = AutoTokenizer.from_pretrained("unhcr/hatespeech-detection")
+    model = AutoModelForSequenceClassification.from_pretrained("unhcr/hatespeech-detection")
+
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors='pt')
+
+    # Perform inference
+    outputs = model(**inputs)
+
+    # Get predicted label
+    predicted_label_idx = torch.argmax(outputs.logits).item()
+    predicted_label = model.config.id2label[predicted_label_idx]
+    return predicted_label
+
+def text_analysis(text):  
+    label_1 = get_hatespeech_score(text)
+    html = '''<!doctype html>
+            <html>
+            <body>
+            <h1>Text Sentiment Analysis</h1>
+            <div style=background-color:#d9eee1>
+            <h2>Overall Sentiment</h2>
+            <p>{}</p>
+            </div>
+            <div style=background-color:#fff4a3>
+            <h2>Adult Content</h2>
+            <p>{}</p>
+            </div>
+            <div style=background-color:#ffc0c7>
+            <h2>Hate Speech</h2>
+            <p>{}</p>
+            </div>
+            <div style=background-color:#cfb0b1>
+            <h2>Text Summary</h2>
+            <p>{}</p>
+            </div>
+            </body>
+            </html>
+            '''.format("Alpha", label_1, "Gamma", "Theta")
+
+    doc = nlp(text)
+    pos_tokens = []
+    for token in doc:
+        pos_tokens.extend([(token.text, token.pos_), (" ", None)])
+
+    return pos_tokens, html
+
+demo = gr.Interface(
+    text_analysis,
+    gr.Textbox(placeholder="Enter sentence here..."),
+    ["highlight", "html"],
+    examples=[
+        ["What a beautiful morning for a walk!"],
+        ["It was the best of times, it was the worst of times."],
+    ],
 )
 
-iface.launch()
+demo.launch()