Update app.py

app.py

@@ -1,135 +1,31 @@
-from transformers import pipeline
 import gradio as gr
-import time  # To simulate processing time for demonstration
-
-# Load the summarization pipelines
-arabic_summarizer = pipeline("summarization", model="eslamxm/mt5-base-finetuned-persian-finetuned-persian-arabic")
-english_summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-# Initialize the current language
-current_language = 'Arabic'
-
-def summarize(text, max_length, min_length, progress=gr.Progress()):
-    global current_language
-    # Simulate a delay to show the progress bar
-    for step in range(10):
-        time.sleep(0.1)  # Simulating processing time
-        progress(step / 10)  # Update the progress bar
-    
-    # Select the summarizer based on the current language
-    if current_language == 'Arabic':
-        summary = arabic_summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
-    else:
-        summary = english_summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
-    
-    return summary
+from transformers import pipeline
+import torch 
 
-def toggle_language():
-    global current_language
-    if current_language == 'Arabic':
-        current_language = 'English'
-        return "Switched to English summarization."
-    else:
-        current_language = 'Arabic'
-        return "تم التبديل إلى تلخيص اللغة العربية."
+# Load sentiment analysis models
+english_sentiment_model = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+arabic_sentiment_model = pipeline("sentiment-analysis", model="akhooli/arabic-sentiment")
 
-def update_interface():
-    if current_language == 'Arabic':
-        return {
-            "title": "أداة تلخيص النصوص",
-            "textbox_label": "ادخل النص هنا",
-            "max_length_label": "الحد الأقصى لطول الملخص",
-            "min_length_label": "الحد الأدنى لطول الملخص",
-            "summarize_button_label": "تلخيص",
-            "summary_label": "الملخص"
-        }
+def analyze_sentiment(text, language):
+    if language == "English":
+        result = english_sentiment_model(text)
     else:
-        return {
-            "title": "Text Summarization Tool",
-            "textbox_label": "Enter Text Here",
-            "max_length_label": "Max Summary Length",
-            "min_length_label": "Min Summary Length",
-            "summarize_button_label": "Summarize",
-            "summary_label": "Summary"
-        }
-
-# Custom CSS for the progress bar with animations
-custom_css = """
-body {
-    background: linear-gradient(135deg, #667eea, #764ba2);
-    font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
-    color: #333;
-    margin: 0;
-    padding: 0;
-}
-.gradio-container {
-    background: rgba(255, 255, 255, 0.95);
-    border-radius: 15px;
-    padding: 30px 40px;
-    box-shadow: 0 8px 30px rgba(0, 0, 0, 0.3);
-    margin: 40px auto;
-    max-width: 1200px;
-}
-.gradio-container h1 {
-    color: #333;
-    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.2);
-}
-.fillable { 
-    width: 95% !important; 
-    max-width: unset !important;
-}
-#examples_container {
-    margin: auto;
-    width: 90%;
-}
-#examples_row {
-    justify-content: center;
-}
-.sidebar {
-    background: rgba(255, 255, 255, 0.98);
-    border-radius: 10px;
-    padding: 20px;
-    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
-}
-button, .btn {
-    background: linear-gradient(90deg, #ff8a00, #e52e71);
-    border: none;
-    color: #fff;
-    padding: 12px 24px;
-    text-transform: uppercase;
-    font-weight: bold;
-    letter-spacing: 1px;
-    border-radius: 5px;
-    cursor: pointer;
-    transition: transform 0.2s ease-in-out;
-}
-button:hover, .btn:hover {
-    transform: scale(1.05);
-}
-"""
-
-# Create the Gradio interface
-with gr.Blocks(css=custom_css) as interface:
-    title = gr.Markdown("<h1 style='text-align: center;'>Text Summarization Tool</h1>")
-    
-    text_input = gr.Textbox(label="Enter Text Here", lines=8, text_align="right")
-    max_length = gr.Slider(minimum=80, maximum=200, value=100, step=1, label="Max Summary Length")
-    min_length = gr.Slider(minimum=5, maximum=50, value=20, step=1, label="Min Summary Length")
-    
-    summarize_button = gr.Button("Summarize")
-    language_button = gr.Button("Switch Language")
-    summary_output = gr.Textbox(label="Summary", lines=6, text_align="right")
-
-    summarize_button.click(summarize, inputs=[text_input, max_length, min_length], outputs=summary_output)
-    
-    def update_labels():
-        labels = update_interface()
-        return labels["title"], labels["textbox_label"], labels["max_length_label"], labels["min_length_label"], labels["summarize_button_label"], labels["summary_label"]
-
-    language_button.click(
-        lambda: (toggle_language(), update_labels()),
-        outputs=[title, text_input, max_length, min_length, summarize_button, summary_output]
-    )
-
-if __name__ == "__main__":
-    interface.launch()
+        result = arabic_sentiment_model(text)
+    return result[0]['label'], result[0]['score']
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=[
+        gr.inputs.Textbox(label="Enter text"),
+        gr.inputs.Radio(choices=["English", "Arabic"], label="Select Language")
+    ],
+    outputs=[
+        gr.outputs.Label(label="Sentiment"),
+        gr.outputs.Number(label="Confidence Score")
+    ],
+    title="Sentiment Analysis",
+    description="Analyze the sentiment of text in English and Arabic."
+)
+
+iface.launch()