Update app.py

app.py

@@ -1,20 +1,52 @@
-import os
-import sys
-
 import gradio as gr
-from gradio.inputs import Textbox
-from gradio.outputs import Textbox as TextOutput
-
-def process(text):
-    print('Hello')
-    sys.stdout.flush()
-    return f'Hi {text} (API_KEY: {os.environ.get("API_KEY")})'
-
-interface = gr.Interface(
-    fn=process,
-    inputs=[Textbox()],
-    outputs=[TextOutput()],
-    theme='huggingface',
-)
-
-interface.launch()
+import torch
+
+import torch.nn.functional as F
+from transformers import BertTokenizer, BertForSequenceClassification
+
+
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+model.load_state_dict(torch.load('/content/Fake-news-detection-bert-based-uncased/model_after_train.pt', map_location=torch.device('cpu')), strict=False)
+model.eval()
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+
+def preprocess_text(text):
+    parts = []
+
+    text_len = len(text.split(' '))
+    delta = 300
+    max_parts = 5
+    nb_cuts = int(text_len / delta)
+    nb_cuts = min(nb_cuts, max_parts)
+    
+    
+    for i in range(nb_cuts + 1):
+        text_part = ' '.join(text.split(' ')[i * delta: (i + 1) * delta])
+        parts.append(tokenizer.encode(text_part, return_tensors="pt", max_length=500).to(device))
+
+    return parts
+
+def test(text):
+    text_parts = preprocess_text(text)
+    overall_output = torch.zeros((1,2)).to(device)
+    try:
+        for part in text_parts:
+            if len(part) > 0:
+                overall_output += model(part.reshape(1, -1))[0]
+    except RuntimeError:
+        print("GPU out of memory, skipping this entry.")
+
+    overall_output = F.softmax(overall_output[0], dim=-1)
+
+    value, result = overall_output.max(0)
+
+    term = "fake"
+    if result.item() == 0:
+        term = "real"
+
+    return term + " at " + str(int(value.item()*100)) + " %"
+
+
+iface = gr.Interface(fn=test, inputs="text", outputs="text")
+iface.launch()