siste

app.py

@@ -3,6 +3,8 @@ from fastai.text.all import load_learner
 import os
 import pathlib
 
+import torch
+
 ## Workaround for Windows Path - PathPosix issue
 temp = pathlib.PosixPath
 pathlib.PosixPath = pathlib.WindowsPath
@@ -12,7 +14,21 @@ def predict(text):
     model_path = "models/LikeItOrNot-base-v1.pkl"
     learner = load_learner(model_path)
     prediction = learner.predict(text)
-    return prediction[0]
+    
+    confidence_score = torch.tensor(prediction[2]).max()
+    confidence= f"{confidence_score:.2%}"
+    
+    if str(prediction[0]) == "1":
+        label = "negative"
+    elif str(prediction[0]) == "2":
+        label = "positive"
+    else:
+        label = "dunno"
+        
+    output = f"Prediction: {label} and Confidence: {confidence}"
+
+    print(prediction)
+    return output, confidence
 
-iface = gr.Interface(fn=predict, inputs="text", outputs="label")
+iface = gr.Interface(fn=predict, inputs="text", outputs=["label", "text"])
 iface.launch()

requirements.txt

@@ -1,2 +1,3 @@
 gradio
-fastai
+fastai
+torch

test.py

@@ -0,0 +1,22 @@
+from fastai.text.all import load_learner
+import pathlib
+
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+model_path = "models/LikeItOrNot-base-v1.pkl"
+learner = load_learner(model_path)
+text = "I love this movie!"
+prediction = learner.predict(text)
+if str(prediction[0]) == "1":
+    print("negative")
+elif str(prediction[0]) == "2":
+    print("positive")
+else:
+    print("dunno")
+
+print("------")
+print(prediction[0])
+print(type(prediction[0]))
+print("------")
+print(prediction)