Create app.py

app.py

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+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+import gradio as gr
+import torch
+from concurrent.futures import ThreadPoolExecutor
+from threading import Lock
+
+# Global cache and lock for thread-safety
+CACHE_SIZE = 100
+prediction_cache = {}
+cache_lock = Lock()
+
+# Mapping for sentiment labels from cardiffnlp/twitter-roberta-base-sentiment
+SENTIMENT_LABEL_MAPPING = {
+    "LABEL_0": "negative",
+    "LABEL_1": "neutral",
+    "LABEL_2": "positive"
+}
+
+def load_model(model_name):
+    """
+    Loads the model with 8-bit quantization if a GPU is available;
+    otherwise, loads the full model.
+    """
+    if torch.cuda.is_available():
+        model = AutoModelForSequenceClassification.from_pretrained(
+            model_name, load_in_8bit=True, device_map="auto"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        device = 0  # GPU index
+    else:
+        model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        device = -1
+    return pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)
+
+# Load both models concurrently at startup.
+with ThreadPoolExecutor() as executor:
+    sentiment_future = executor.submit(load_model, "cardiffnlp/twitter-roberta-base-sentiment")
+    emotion_future = executor.submit(load_model, "bhadresh-savani/bert-base-uncased-emotion")
+
+sentiment_pipeline = sentiment_future.result()
+emotion_pipeline = emotion_future.result()
+
+def analyze_text(text):
+    # Check cache first (thread-safe)
+    with cache_lock:
+        if text in prediction_cache:
+            return prediction_cache[text]
+    
+    try:
+        # Run both model inferences in parallel.
+        with ThreadPoolExecutor() as executor:
+            future_sentiment = executor.submit(sentiment_pipeline, text)
+            future_emotion = executor.submit(emotion_pipeline, text)
+            sentiment_result = future_sentiment.result()[0]
+            emotion_result = future_emotion.result()[0]
+        
+        # Remap the sentiment label to a human-readable format if available.
+        raw_sentiment_label = sentiment_result.get("label", "")
+        sentiment_label = SENTIMENT_LABEL_MAPPING.get(raw_sentiment_label, raw_sentiment_label)
+        
+        # Format the output with rounded scores.
+        result = {
+            "Sentiment": {sentiment_label: round(sentiment_result['score'], 4)},
+            "Emotion": {emotion_result['label']: round(emotion_result['score'], 4)}
+        }
+    except Exception as e:
+        result = {"error": str(e)}
+    
+    # Update the cache in a thread-safe manner.
+    with cache_lock:
+        if len(prediction_cache) >= CACHE_SIZE:
+            prediction_cache.pop(next(iter(prediction_cache)))
+        prediction_cache[text] = result
+    
+    return result
+
+# Define the Gradio interface.
+demo = gr.Interface(
+    fn=analyze_text,
+    inputs=gr.Textbox(placeholder="Enter your text here...", label="Input Text"),
+    outputs=gr.JSON(label="Analysis Results"),
+    title="🚀 Fast Sentiment & Emotion Analysis",
+    description="Optimized application that remaps sentiment labels and uses parallel processing.",
+    examples=[
+        ["I'm thrilled to start this new adventure!"],
+        ["This situation is making me really frustrated."],
+        ["I feel so heartbroken and lost."]
+    ],
+    theme="soft",
+    allow_flagging="never"
+)
+
+# Warm up the models with a sample input.
+_ = analyze_text("Warming up models...")
+
+if __name__ == "__main__":
+    # Bind to all interfaces for Hugging Face Spaces.
+    demo.launch(server_name="0.0.0.0", server_port=7860)