from transformers import pipeline
import gradio as gr

# Pipeline
pipe = pipeline("text-classification", model="AbrorBalxiyev/my_awesome_model", return_all_scores=True)

# def get_html_for_results(results):
#     # Sort results by score in descending order
#     sorted_results = sorted(results, key=lambda x: x['score'], reverse=True)
    
#     html = """
#     <style>
#         .result-container {
#             font-family: Arial, sans-serif;
#             max-width: 600px;
#             margin: 20px auto;
#         }
#         .category-row {
#             margin: 10px 0;
#         }
#         .category-name {
#             display: inline-block;
#             width: 120px;
#             font-size: 14px;
#             color: #333;
#         }
#         .progress-bar {
#             display: inline-block;
#             width: calc(100% - 200px);
#             height: 20px;
#             background-color: #f0f0f0;
#             border-radius: 10px;
#             overflow: hidden;
#             margin-right: 10px;
#         }
#         .progress {
#             height: 100%;
#             background-color: #ff6b33;
#             border-radius: 10px;
#             transition: width 0.5s ease-in-out;
#         }
#         .percentage {
#             display: inline-block;
#             width: 50px;
#             text-align: right;
#             color: #666;
#         }
#     </style>
#     <div class="result-container">
#     """
    
#     for item in sorted_results:
#         percentage = item['score'] * 100
#         html += f"""
#         <div class="category-row">
#             <span class="category-name">{item['label']}</span>
#             <div class="progress-bar">
#                 <div class="progress" style="width: {percentage}%;"></div>
#             </div>
#             <span class="percentage">{percentage:.0f}%</span>
#         </div>
#         """
    
#     html += "</div>"
#     return html

# # Gradio interfeysi uchun funksiyani qayta yozish
# def classify_text(text):
#     if not text.strip():
#         return "Please enter some text to classify."

#     pred = pipe(text)
#     return get_html_for_results(pred[0])
    
# # Gradio interfeysi
# iface = gr.Interface(
#     fn=classify_text,
#     inputs=[
#         gr.Textbox(
#             placeholder="Enter text to classify...",
#             label=None,
#             lines=3
#         )
#     ],
#     outputs=gr.HTML(),
#     title="Text Category Classification",
#     css="""
#         .gradio-container {
#             font-family: Arial, sans-serif;
#         }
#         .gradio-interface {
#             max-width: 800px !important;
#         }
#         #component-0 {
#             border-radius: 8px;
#             border: 1px solid #ddd;
#         }
#         .submit-button {
#             background-color: #ff6b33 !important;
#         }
#         .clear-button {
#             background-color: #f0f0f0 !important;
#             color: #333 !important;
#         }
#     """,
#     examples=[
#         ["Messi jahon chempioni bo'ldi"],
#         ["Yangi iPhone 15 Pro Max sotuvga chiqdi"],
#         ["Kitob o'qish foydali"],
#         ["Toshkentda ob-havo issiq"]
#     ]
# )

# iface.launch(share=True)
demo=gr.Interface.from_pipeline(pipe)
demo.launch(debug=True)