Update app.py

app.py

@@ -1,4 +1,4 @@
-import sklearn
+from os import O_ACCMODE
 import gradio as gr
 import joblib
 from transformers import pipeline
@@ -6,33 +6,45 @@ import requests.exceptions
 from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.repocard import metadata_load
 
+# work around for error, not happy really
+# import os
+# os.environ['KMP_DUPLICATE_LIB_OK']='True'
+
 app = gr.Blocks()
 
+model_1 = "juliensimon/distilbert-amazon-shoe-reviews"
+model_2 = "juliensimon/distilbert-amazon-shoe-reviews"
+
 def load_agent(model_id_1, model_id_2):
     """
     This function load the agent's results
     """
     # Load the metrics
     metadata_1 = get_metadata(model_id_1)
+
+    # get predictions
+    predictions_1 = predict(model_id_1)
     
     # Get the accuracy
-    results_1 = parse_metrics_accuracy(metadata_1)
+    # results_1 = parse_metrics_accuracy(metadata_1)
     
     # Load the metrics
     metadata_2 = get_metadata(model_id_2)
-    
+
+    # get predictions
+    predictions_2 = predict(model_id_2)
     # Get the accuracy
-    results_2 = parse_metrics_accuracy(metadata_2)
+    # results_2 = parse_metrics_accuracy(metadata_2)
 
-    return model_id_1, results_1, model_id_2, results_2
+    return model_id_1, predictions_1, model_id_2, predictions_2
 
-def parse_metrics_accuracy(meta):
-    if "model-index" not in meta:
-        return None
-    result = meta["model-index"][0]["results"]
-    metrics = result[0]["metrics"]
-    accuracy = metrics[0]["value"]
-    return accuracy
+# def parse_metrics_accuracy(meta):
+#     if "model-index" not in meta:
+#         return None
+#     result = meta["model-index"][0]["results"]
+#     metrics = result[0]["metrics"]
+#     accuracy = metrics[0]["value"]
+#     return accuracy
 
 def get_metadata(model_id):
     """
@@ -48,9 +60,10 @@ def get_metadata(model_id):
     except requests.exceptions.HTTPError:
         return None
 
-classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
+# classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
                 
-def predict(review):
+def predict(review, model_id):
+        classifier = pipeline("text-classification", model=model_id)
         prediction = classifier(review)
         print(prediction)
         stars = prediction[0]['label']
@@ -66,34 +79,33 @@ with app:
     Type text to predict sentiment.
     """)   
     with gr.Row():
-        inp = gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
-        out = gr.Textbox(label="Prediction")
-    with gr.Row():
-        btn = gr.Button("Predict")
-    btn.click(fn=predict, inputs=inp, outputs=out)
+        inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
+        out_2 = gr.Textbox(label="Prediction")
+
     
-    gr.Markdown(
-    """
-    Type two model's id you want to compare or check examples below.
-    """)
+    # gr.Markdown(
+    # """
+    # Model Predictions
+    # """)
     with gr.Row():
       model1_input = gr.Textbox(label="Model 1")
-      model2_input = gr.Textbox(label="Model 2") 
     with gr.Row():
-      app_button = gr.Button("Compare models")
+        btn = gr.Button("Prediction for Model 1")
+    btn.click(fn=predict(model_1), inputs=inp_1, outputs=out_2)
+
+
+
+    with gr.Row():
+      model2_input = gr.Textbox(label="Model 2") 
     with gr.Row():
-      with gr.Column():
-        model1_name = gr.Markdown()
-        model1_score_output = gr.Textbox(label="Sentiment")
-      with gr.Column():
-        model2_name = gr.Markdown()
-        model2_score_output = gr.Textbox(label="Sentiment")
+        btn = gr.Button("Prediction for Model 2")
+    btn.click(fn=predict(model_2), inputs=inp_1, outputs=out_2)
+
 
     app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
     
-    examples = gr.Examples(examples=[["distilbert-base-uncased-finetuned-sst-2-english","distilbert-base-uncased-finetuned-sst-2-english"],
-        ["distilbert-base-uncased-finetuned-sst-2-english", "distilbert-base-uncased-finetuned-sst-2-english"]],
-                           inputs=[model1_input, model2_input])
+    # examples = gr.Examples(examples=[["juliensimon/distilbert-amazon-shoe-reviews","juliensimon/distilbert-amazon-shoe-reviews"]],
+    #                        inputs=[model1_input, model2_input])
 
     
 app.launch()