Upload app.py

app.py

@@ -1,184 +1,45 @@
-import os
-import gradio as gr
+_H='custom'
+_G='primary'
+_F='e.g., business, technology, sports, entertainment'
+_E='Custom Labels (for custom classification)'
+_D='Classification Type:'
+_C='sentiment'
+_B='Spam'
+_A='Sentiment'
+import os,gradio as gr
 from openai import OpenAI
-
-API_KEY = os.environ['API_KEY']
-# Initialize client
-client = OpenAI(
-    base_url="https://openrouter.ai/api/v1",
-    api_key=API_KEY
-)
-
-def classify_text(text, classification_type="sentiment", custom_labels=""):
-    """
-    Classify text using OpenRouter's GPT-OSS-20B model
-    """
-    if not text.strip():
-        return "Please enter some text to classify."
-    
-    # Define classification prompts based on type
-    if classification_type == "Sentiment":
-        prompt = f"Classify the sentiment of the following text as Positive, Negative, or Neutral. Only respond with one word: Positive, Negative, or Neutral.\n\nText: {text}"
-    # elif classification_type == "topic":
-    #     prompt = f"Classify the main topic/category of the following text. Choose the most appropriate category and respond with just the category name.\n\nText: {text}"
-    elif classification_type == "Spam":
-        prompt = f"Classify whether the following text is Spam or Not Spam. Only respond with: Spam or Not Spam.\n\nText: {text}"
-    # elif classification_type == "custom":
-    #     if not custom_labels.strip():
-    #         return "Please provide custom labels for classification."
-    #     prompt = f"Classify the following text into one of these categories: {custom_labels}. Only respond with one of the provided categories.\n\nText: {text}"
-    # else:
-    #     prompt = f"Classify the following text and provide a brief classification result.\n\nText: {text}"
-    
-    try:
-        response = client.chat.completions.create(
-            model="openai/gpt-oss-20b",
-            messages=[
-                {"role": "system", "content": "You are a text classification assistant. Provide concise, accurate classifications."},
-                {"role": "user", "content": prompt}
-            ],
-            max_tokens=50,
-            temperature=0.1,
-            extra_headers={
-                "Authorization": f"Bearer {API_KEY}",
-                "HTTP-Referer": "https://your-app-url.com",
-                "X-Title": ""
-            }
-        )
-        
-        classification_result = response.choices[0].message.content.strip()
-        return f"Classification Result: {classification_result}"
-        
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-def batch_classify(file, classification_type="sentiment", custom_labels=""):
-    """
-    Classify multiple texts from uploaded file
-    """
-    if file is None:
-        return "Please upload a text file."
-    
-    try:
-        # Read file content
-        with open(file.name, 'r', encoding='utf-8') as f:
-            lines = f.readlines()
-        
-        results = []
-        for i, line in enumerate(lines[:10], 1):  # Limit to first 10 lines
-            line = line.strip()
-            if line:
-                result = classify_text(line, classification_type, custom_labels)
-                results.append(f"{i}. **Text:** {line}\n   **Result:** {result}\n")
-        
-        return "\n".join(results) if results else "No text found in file."
-        
-    except Exception as e:
-        return f"Error processing file: {str(e)}"
-
-# Create Gradio interface
-with gr.Blocks(
-    title="",
-    #theme=gr.themes.Soft()
-    theme=gr.themes.Default(primary_hue="sky")
-
-) as demo:
-    with gr.Tabs():
-        # Single Text Classification Tab
-        with gr.Tab("Single Text"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    text_input = gr.Textbox(
-                        label="",
-                        placeholder="Enter text to classify...",
-                        lines=4
-                    )
-                    
-                    classification_type = gr.Radio(
-                        choices=["Sentiment", "Spam"],
-                        value="Sentiment",
-                        label="Classification Type:"
-                    )
-                    
-                    custom_labels = gr.Textbox(
-                        label="Custom Labels (for custom classification)",
-                        placeholder="e.g., business, technology, sports, entertainment",
-                        visible=False
-                    )
-                    
-                    classify_btn = gr.Button("Classify Text", variant="primary")
-                
-                with gr.Column(scale=2):
-                    single_output = gr.Markdown(
-                        value=""
-                    )
-            
-            # Show/hide custom labels based on selection
-            def toggle_custom_labels(choice):
-                return gr.update(visible=(choice == "custom"))
-            
-            classification_type.change(
-                toggle_custom_labels,
-                inputs=[classification_type],
-                outputs=[custom_labels]
-            )
-            
-            classify_btn.click(
-                classify_text,
-                inputs=[text_input, classification_type, custom_labels],
-                outputs=[single_output]
-            )
-        
-        # Batch Classification Tab
-        with gr.Tab("Batch Classification"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    gr.Markdown("Upload a text or csv file:")
-                    
-                    file_input = gr.File(
-                        label="Upload File",
-                        file_types=[".txt", ".csv"]
-                    )
-                    
-                    batch_classification_type = gr.Radio(
-                        choices=["Sentiment", "Spam"],
-                        value="Sentiment",
-                        label="Classification Type:"
-                    )
-                    
-                    batch_custom_labels = gr.Textbox(
-                        label="Custom Labels (for custom classification)",
-                        placeholder="e.g., business, technology, sports, entertainment",
-                        visible=False
-                    )
-                    
-                    batch_classify_btn = gr.Button("🔍 Classify Batch", variant="primary")
-                
-                with gr.Column(scale=2):
-                    batch_output = gr.Markdown(
-                        value=""
-                    )
-            
-            def toggle_batch_custom_labels(choice):
-                return gr.update(visible=(choice == "custom"))
-            
-            batch_classification_type.change(
-                toggle_batch_custom_labels,
-                inputs=[batch_classification_type],
-                outputs=[batch_custom_labels]
-            )
-            
-            batch_classify_btn.click(
-                batch_classify,
-                inputs=[file_input, batch_classification_type, batch_custom_labels],
-                outputs=[batch_output]
-            )
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,
-        show_error=True
-    )
+API_KEY=os.environ['API_KEY']
+client=OpenAI(base_url='https://openrouter.ai/api/v1',api_key=API_KEY)
+def classify_text(text,classification_type=_C,custom_labels=''):
+	"\n    Classify text using OpenRouter's GPT-OSS-20B model\n    ";E='content';D='role';B=classification_type;A=text
+	if not A.strip():return'Please enter some text to classify.'
+	if B==_A:C=f"Classify the sentiment of the following text as Positive, Negative, or Neutral. Only respond with one word: Positive, Negative, or Neutral.\n\nText: {A}"
+	elif B==_B:C=f"Classify whether the following text is Spam or Not Spam. Only respond with: Spam or Not Spam.\n\nText: {A}"
+	try:F=client.chat.completions.create(model='openai/gpt-oss-20b',messages=[{D:'system',E:'You are a text classification assistant. Provide concise, accurate classifications.'},{D:'user',E:C}],max_tokens=50,temperature=.1,extra_headers={'Authorization':f"Bearer {API_KEY}",'HTTP-Referer':'https://your-app-url.com','X-Title':''});G=F.choices[0].message.content.strip();return f"Classification Result: {G}"
+	except Exception as H:return f"Error: {str(H)}"
+def batch_classify(file,classification_type=_C,custom_labels=''):
+	'\n    Classify multiple texts from uploaded file\n    '
+	if file is None:return'Please upload a text file.'
+	try:
+		with open(file.name,'r',encoding='utf-8')as C:D=C.readlines()
+		B=[]
+		for(E,A)in enumerate(D[:10],1):
+			A=A.strip()
+			if A:F=classify_text(A,classification_type,custom_labels);B.append(f"{E}. **Text:** {A}\n   **Result:** {F}\n")
+		return'\n'.join(B)if B else'No text found in file.'
+	except Exception as G:return f"Error processing file: {str(G)}"
+with gr.Blocks(title='',theme=gr.themes.Default(primary_hue='sky'))as demo:
+	with gr.Tabs():
+		with gr.Tab('Single Text'):
+			with gr.Row():
+				with gr.Column(scale=2):text_input=gr.Textbox(label='',placeholder='Enter text to classify...',lines=4);classification_type=gr.Radio(choices=[_A,_B],value=_A,label=_D);custom_labels=gr.Textbox(label=_E,placeholder=_F,visible=False);classify_btn=gr.Button('Classify Text',variant=_G)
+				with gr.Column(scale=2):single_output=gr.Markdown(value='')
+			def toggle_custom_labels(choice):return gr.update(visible=choice==_H)
+			classification_type.change(toggle_custom_labels,inputs=[classification_type],outputs=[custom_labels]);classify_btn.click(classify_text,inputs=[text_input,classification_type,custom_labels],outputs=[single_output])
+		with gr.Tab('Batch Classification'):
+			with gr.Row():
+				with gr.Column(scale=2):gr.Markdown('Upload a text or csv file:');file_input=gr.File(label='Upload File',file_types=['.txt','.csv']);batch_classification_type=gr.Radio(choices=[_A,_B],value=_A,label=_D);batch_custom_labels=gr.Textbox(label=_E,placeholder=_F,visible=False);batch_classify_btn=gr.Button('🔍 Classify Batch',variant=_G)
+				with gr.Column(scale=2):batch_output=gr.Markdown(value='')
+			def toggle_batch_custom_labels(choice):return gr.update(visible=choice==_H)
+			batch_classification_type.change(toggle_batch_custom_labels,inputs=[batch_classification_type],outputs=[batch_custom_labels]);batch_classify_btn.click(batch_classify,inputs=[file_input,batch_classification_type,batch_custom_labels],outputs=[batch_output])
+if __name__=='__main__':demo.launch(server_name='0.0.0.0',server_port=7860,share=True,show_error=True)