Update app.py

app.py

@@ -1,99 +1,99 @@
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
-import gradio as gr
-import torch
-from concurrent.futures import ThreadPoolExecutor
-from threading import Lock
+# 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()
+# # 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"
-}
+# # 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)
+# 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")
+# # 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()
+# 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]
+# 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]
+#     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)
+#         # 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)}
+#         # 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
+#     # 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
+#     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"
-)
+# # 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...")
+# # 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)
+# if __name__ == "__main__":
+#     # Bind to all interfaces for Hugging Face Spaces.
+#     demo.launch(server_name="0.0.0.0", server_port=7860)