Update app.py

app.py

@@ -1,11 +1,12 @@
 import gradio as gr
 from transformers import pipeline
+import numpy as np
 
-# Sentiment analysis model'ini yükle
+# Daha hafif ve hızlı sentiment analysis model
+# distilbert-base-uncased-finetuned-sst-2-english -> Hafif ve hızlı
 sentiment_pipeline = pipeline(
     "sentiment-analysis",
-    model="cardiffnlp/twitter-roberta-base-sentiment-latest",
-    top_k=None
+    model="distilbert-base-uncased-finetuned-sst-2-english"
 )
 
 def analyze_sentiment(text):
@@ -27,38 +28,29 @@ def analyze_sentiment(text):
     
     try:
         # Model'den tahmin al
-        results = sentiment_pipeline(text)[0]
+        result = sentiment_pipeline(text)[0]
         
-        # En yüksek skora sahip sentiment'i bul
-        top_result = max(results, key=lambda x: x['score'])
+        label = result['label'].lower()
+        score = result['score']
         
-        # Label'ı normalize et (positive, negative, neutral)
-        label = top_result['label'].lower()
-        score = top_result['score']
-        
-        # Label mapping (model'e göre değişebilir)
-        sentiment_map = {
-            'positive': 'positive',
-            'pos': 'positive',
-            'negative': 'negative',
-            'neg': 'negative',
-            'neutral': 'neutral',
-            'neu': 'neutral'
-        }
-        
-        sentiment = sentiment_map.get(label, 'neutral')
-        
-        # Negative için score'u normalize et
-        if sentiment == 'negative':
-            score = 1.0 - score
+        # Label mapping
+        if 'pos' in label:
+            sentiment = 'positive'
+        elif 'neg' in label:
+            sentiment = 'negative'
+            score = 1.0 - score  # Negative için score'u ters çevir
+        else:
+            sentiment = 'neutral'
         
         return {
             "sentiment": sentiment,
             "score": round(score, 4),
-            "all_scores": results
+            "raw_label": result['label'],
+            "raw_score": result['score']
         }
         
     except Exception as e:
+        print(f"Error in sentiment analysis: {str(e)}")
         return {
             "error": str(e),
             "sentiment": "neutral",
@@ -69,7 +61,7 @@ def analyze_sentiment(text):
 def gradio_interface(text):
     result = analyze_sentiment(text)
     
-    if "error" in result:
+    if "error" in result and result["error"] != "Please provide a text to analyze":
         return f"⚠️ Error: {result['error']}\n\nDefault: {result['sentiment']} ({result['score']})"
     
     sentiment = result['sentiment']
@@ -85,35 +77,60 @@ def gradio_interface(text):
     emoji = emoji_map.get(sentiment, '😐')
     
     output = f"{emoji} **Sentiment:** {sentiment.upper()}\n"
-    output += f"📊 **Score:** {score}\n\n"
-    output += "**All Scores:**\n"
+    output += f"📊 **Confidence Score:** {score}\n\n"
     
-    for item in result['all_scores']:
-        output += f"- {item['label']}: {item['score']:.4f}\n"
+    if "raw_label" in result:
+        output += f"**Raw Model Output:**\n"
+        output += f"- Label: {result['raw_label']}\n"
+        output += f"- Score: {result['raw_score']:.4f}\n"
     
     return output
 
-# Gradio UI
-demo = gr.Interface(
-    fn=gradio_interface,
-    inputs=gr.Textbox(
-        lines=3,
-        placeholder="Enter text to analyze sentiment...",
-        label="Input Text"
-    ),
-    outputs=gr.Markdown(label="Sentiment Analysis Result"),
-    title="🤖 Sentiment Analysis API",
-    description="Real-time sentiment analysis using Hugging Face Transformers. Analyzes text and returns positive, negative, or neutral sentiment with confidence scores.",
-    examples=[
-        ["This is amazing! I love it!"],
-        ["I'm so sad and disappointed."],
-        ["The weather is okay today."],
-        ["Bu harika bir gün! Çok mutluyum."],
-        ["Berbat bir deneyimdi, hiç beğenmedim."]
-    ],
-    theme=gr.themes.Soft(),
-    api_name="analyze"
-)
+# API fonksiyonu (JSON response)
+def api_analyze(text):
+    """
+    API endpoint için JSON response döner
+    """
+    return analyze_sentiment(text)
 
-if __name__ == "__main__":
-    demo.launch()
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 Sentiment Analysis API")
+    gr.Markdown("Real-time sentiment analysis using Hugging Face Transformers.")
+    
+    with gr.Tab("Web Interface"):
+        with gr.Row():
+            with gr.Column():
+                input_text = gr.Textbox(
+                    lines=3,
+                    placeholder="Enter text to analyze sentiment...",
+                    label="Input Text"
+                )
+                analyze_btn = gr.Button("Analyze", variant="primary")
+            
+            with gr.Column():
+                output_markdown = gr.Markdown(label="Result")
+        
+        gr.Examples(
+            examples=[
+                ["This is amazing! I love it!"],
+                ["I'm so sad and disappointed."],
+                ["The weather is okay today."],
+                ["This product is terrible, worst purchase ever!"],
+                ["Great service, highly recommend!"]
+            ],
+            inputs=input_text
+        )
+        
+        analyze_btn.click(
+            fn=gradio_interface,
+            inputs=input_text,
+            outputs=output_markdown
+        )
+    
+    with gr.Tab("API Usage"):
+        gr.Markdown("""
+        ## API Endpoint
+        
+        Use this endpoint to analyze sentiment programmatically:
+```python