firts try test

app.py

@@ -0,0 +1,59 @@
+# run this to import all needed libraries
+!pip install -U duckduckgo_search
+!pip install firebase-admin
+!pip install gradio
+
+from fastai import *
+from fastdownload import download_url
+from fastai.vision.all import *
+from duckduckgo_search import ddg_images
+from fastcore.all import *
+import gradio as gr
+
+
+# ref = db.reference("/")
+path = Path()
+model = load_learner(path/"Emotionv2.pkl")
+#working process , unpickling
+# något sätt att ladda upp filer
+# modellen visar de bilder den analyserar och vad den klassificierar
+
+labelA = "Angry human face"
+labelB = "Disgusted human face"
+labelC = "Happy human face"
+labelD = "Sad human face"
+
+labels = ["Angry human face", "Disgusted human face", "Happy human face", "Sad human face"]
+
+# this is where we use the model on a given file and get the classification and probability for it that the model gives
+# img = PILImage.create("Sad2.jpg") # insert uploaded file name
+
+def predict(img):
+  img = PILImage.create(img)
+  pred,_,probs = model.predict(img)
+  # a = model.predict(img)
+  return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+
+# print(a)
+# img.show()
+# print(f"this is: {pred}")
+# print(f"{labelA} {probs[0].item():.2f}")
+# print(f"{labelB} {probs[1].item():.2f}")
+# print(f"{labelC} {probs[2].item():.2f}")
+# print(f"{labelD} {probs[3].item():.2f}")
+
+# print(f"Probability of {pred}: {max(probs):.2f}")
+
+interpretation='default'
+
+# gr.Image(source="webcam", streaming=True),
+
+gr.Interface(fn=predict,
+             inputs=gr.inputs.Image(shape=(512, 512)),
+             interpretation=interpretation,
+             outputs=gr.outputs.Label(num_top_classes=4)).launch(share=True)
+
+
+
+