Update app.py

app.py

@@ -1,44 +1,64 @@
 import gradio as gr
+import time
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
-# Load the fine-tuned model and tokenizer
 model_name = "ethanrom/a2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# Load the pretrained model and tokenizer
 pretrained_model_name = "roberta-large-mnli"
 pretrained_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name)
 pretrained_model = pipeline("zero-shot-classification", model=pretrained_model_name, tokenizer=pretrained_tokenizer)
 candidate_labels = ["negative", "positive", "no impact", "mixed"]
 
+accuracy_scores = {"Fine-tuned": 0.0, "Pretrained": 0.0}
+model_sizes = {"Fine-tuned": 0, "Pretrained": 0}
+inference_times = {"Fine-tuned": 0.0, "Pretrained": 0.0}
 
 def predict_sentiment(text_input, model_selection):
+    global accuracy_scores, model_sizes, inference_times
+    
+    start_time = time.time()
     if model_selection == "Fine-tuned":
-        # Use the fine-tuned model
         inputs = tokenizer.encode_plus(text_input, return_tensors='pt')
         outputs = model(**inputs)
         logits = outputs.logits.detach().cpu().numpy()[0]
         predicted_class = int(logits.argmax())
-        return candidate_labels[predicted_class]
+        accuracy_scores[model_selection] += 1 if candidate_labels[predicted_class] == "positive" else 0
+        model_sizes[model_selection] = model.num_parameters()
     else:
-        # Use the pretrained model
         result = pretrained_model(text_input, candidate_labels)
         predicted_class = result["labels"][0]
-        return predicted_class
+        accuracy_scores[model_selection] += 1 if predicted_class == 1 else 0
+        model_sizes[model_selection] = pretrained_model.model.num_parameters()
+    end_time = time.time()
+    inference_times[model_selection] = end_time - start_time
+
+    return candidate_labels[predicted_class]
+
+def accuracy(model_selection):
+    return accuracy_scores[model_selection]/10
+
+def model_size(model_selection):
+    return str(model_sizes[model_selection]//(1024*1024)) + " MB"
+
+def inference_time(model_selection):
+    return str(inference_times[model_selection]*1000) + " ms"
 
 inputs = [
     gr.inputs.Textbox("Enter text"),
     gr.inputs.Dropdown(["Pretrained", "Fine-tuned"], label="Select model"),
 ]
 
-outputs = gr.outputs.Textbox(label="Predicted Sentiment")
+outputs = [
+    gr.outputs.Textbox(label="Predicted Sentiment"),
+    gr.outputs.Label(label="Accuracy:"),
+    gr.outputs.Label(label="Model Size:"),
+    gr.outputs.Label(label="Inference Time:")
+]
 
-gr.Interface(fn=predict_sentiment, inputs=inputs, outputs=outputs, title="Sentiment Analysis", description="Compare the output of two models", examples=[
-        ["max laid his hand upon the old man's arm", "Pretrained Model"],
-        ["the red sword sealed their vows!", "Fine-tuned Model"],
-        ["and that is why, the lonesome day,", "Pretrained Model"],
-        ["it flows so long as falls the rain", "Fine-tuned Model"],
-        ["thy hands all cunning arts that women prize", "Pretrained Model"],
-        ["on us lift up the light", "Fine-tuned Model"],
-    ],).launch();
+gr.Interface(fn=predict_sentiment, inputs=inputs, outputs=outputs, 
+             title="Sentiment Analysis", description="Compare the output of two models",
+             live=True,
+             examples=[["on us lift up the light", "Fine-tuned"], ["max laid his hand upon the old man's arm", "Pretrained"]]
+            ).launch();