Create app.py

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+import pickle
+from keras.models import load_model
+
+model = load_model("model.keras")
+with open("tokenizer.pckl", "rb") as file:
+    tokenizer = pickle.load(file)
+
+def classify(text: str, response: str):
+    question = list(tokenizer.texts_to_sequences([q.lower(),])[0])
+    answer = list(tokenizer.texts_to_sequences([a.lower(),])[0])
+    arr = np.array([(list(question)+[0,]*l1)[:l1]+(list(answer)+[0,]*l2)[:l2],])
+    prediction = model.predict(arr)[0][0]
+    if prediction > 0.9:
+        return "Surely relevant "+str(prediction)
+    elif prediction > 0.5:
+        return "Relevant "+str(prediction)
+    elif prediction > 0.1:
+        return "Probably relevant "+str(prediction)
+    else:
+        return "Irrelevant "+str(prediction)
+
+iface = gr.Interface(fn=pwcheck, inputs=["text", "text"], outputs="text")
+iface.launch()