Update app.py

app.py

@@ -12,20 +12,12 @@ class ONNXInferencePipeline:
         self.onnx_path = hf_hub_download(repo_id=repo_id, filename="mindmeter.onnx")
         self.tokenizer_path = hf_hub_download(repo_id=repo_id, filename="train_bpe_tokenizer.json")
         self.config_path = hf_hub_download(repo_id=repo_id, filename="hyperparameters.json")
-
-        # Load configuration from hyperparameters.json.
-        # This file should have a key "MAX_LEN" specifying the maximum token sequence length.
         with open(self.config_path, "r") as f:
             self.config = json.load(f)
 
-        # Initialize the tokenizer from file.
         self.tokenizer = Tokenizer.from_file(self.tokenizer_path)
-        # Use the maximum sequence length from the configuration.
         self.max_len = self.config["MAX_LEN"]
-
-        # Initialize the ONNX runtime session.
         self.session = ort.InferenceSession(self.onnx_path)
-        # Use CUDA if available, otherwise default to CPU.
         self.providers = ['CPUExecutionProvider']
         if 'CUDAExecutionProvider' in ort.get_available_providers():
             self.providers = ['CUDAExecutionProvider']
@@ -54,24 +46,19 @@ class ONNXInferencePipeline:
         outputs = self.session.run(None, {"input": input_array})
         logits = outputs[0]
 
-        # Compute softmax probabilities from the logits.
         exp_logits = np.exp(logits)
         probabilities = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)
         predicted_class = int(np.argmax(probabilities))
         
-        # Map the predicted index to a label.
         class_labels = ["Not Stressed", "Stressed"]
         predicted_label = class_labels[predicted_class]
 
-        # Return only the predicted stress label.
         return {"stress_level": predicted_label}
 
 
 if __name__ == "__main__":
-    # Initialize the pipeline with the Hugging Face repository ID.
     pipeline = ONNXInferencePipeline(repo_id="iimran/MindMeter")
 
-    # Example input texts for local testing.
     text1 = "Yay! what a happy life"
     text2 = "I’ve missed another loan payment, and I don’t know how I’m going to catch up. The pressure is unbearable."
     text3 = "I am upset about how badly life is trating me these days, its shit."
@@ -84,12 +71,10 @@ if __name__ == "__main__":
     print(f"Prediction for text 2: {result2}")
     print(f"Prediction for text 3: {result3}")
 
-    # Define a function for Gradio to use.
     def gradio_predict(text):
         result = pipeline.predict(text)
         return result["stress_level"]
 
-    # Create the Gradio interface.
     iface = gr.Interface(
         fn=gradio_predict,
         inputs=gr.Textbox(lines=7, placeholder="Enter your text here..."),
@@ -106,5 +91,4 @@ if __name__ == "__main__":
         ]
     )
 
-    # Launch the Gradio app.
     iface.launch()