initial commit

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the sentiment analysis pipeline globally
+# This will download and cache the model on the first run.
+# The default model is 'distilbert-base-uncased-finetuned-sst-2-english'
+try:
+    sentiment_analyzer = pipeline("sentiment-analysis")
+except Exception as e:
+    print(f"Error initializing sentiment analysis pipeline: {e}")
+    sentiment_analyzer = None
+
+def sentiment_analysis(text: str) -> dict:
+    """
+    Analyze the sentiment of the given text using a Hugging Face transformers model.
+
+    Args:
+        text (str): The text to analyze.
+
+    Returns:
+        dict: A dictionary containing the sentiment assessment.
+    """
+    if not sentiment_analyzer:
+        return {
+            "assessment": "error",
+            "polarity": 0.0,
+            "details": "Sentiment analyzer not available."
+        }
+    
+    # Handle empty or whitespace-only input
+    if not text or not text.strip():
+        return {
+            "assessment": "neutral", # Or specific "empty_input"
+            "polarity": 0.0,
+            "model_score": 0.0,
+            "details": "Input text is empty."
+        }
+
+    try:
+        # The pipeline returns a list of dictionaries, e.g., [{'label': 'POSITIVE', 'score': 0.99}]
+        # We take the first result as we are analyzing the whole text as one segment.
+        result = sentiment_analyzer(text)[0] 
+        label = result['label']
+        score = result['score']
+
+        assessment = "neutral" # Default assessment
+        polarity = 0.0
+
+        if label == "POSITIVE":
+            assessment = "positive"
+            polarity = score  # Score is confidence, directly maps to positive polarity
+        elif label == "NEGATIVE":
+            assessment = "negative"
+            # To align with a -1 to 1 range like TextBlob, make polarity negative for negative sentiment
+            polarity = -score 
+        
+        # Note: Subjectivity is not directly provided by this specific transformer model.
+        # We return polarity, assessment, and the raw model score for more detail.
+        return {
+            "polarity": round(polarity, 2),
+            "assessment": assessment,
+            "model_score": round(score, 4) 
+        }
+    except Exception as e:
+        print(f"Error during sentiment analysis for text '{text[:50]}...': {e}")
+        return {
+            "assessment": "error",
+            "polarity": 0.0,
+            "details": f"Error processing text: {str(e)}"
+        }
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Enter text to analyze..."),
+    outputs=gr.JSON(),
+    title="Advanced Text Sentiment Analysis (Transformers)",
+    description="Analyze the sentiment of text using a Hugging Face Transformers model. Provides polarity, assessment, and model score."
+)
+
+# Launch the interface and MCP server
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

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+gradio
+transformers
+torch
+textblob