Copiando proyecto TFG

README.md

@@ -1,8 +1,8 @@
 ---
 title: TFM DATCOM
-emoji: 🌖
-colorFrom: blue
-colorTo: green
+emoji: 📰
+colorFrom: red
+colorTo: blue
 sdk: gradio
 sdk_version: 3.35.2
 app_file: app.py

app.py

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+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+from huggingface_hub import KerasModelHubMixin
+import transformers
+from transformers import AutoTokenizer
+import numpy as np
+
+
+m = from_pretrained_keras('sgonzalezsilot/FakeNews-Detection-Twitter-Thesis')
+
+MODEL = "digitalepidemiologylab/covid-twitter-bert-v2"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+def bert_encode(tokenizer,data,maximum_length) :
+  input_ids = []
+  attention_masks = []
+  
+
+  for i in range(len(data)):
+      encoded = tokenizer.encode_plus(
+        
+        data[i],
+        add_special_tokens=True,
+        max_length=maximum_length,
+        pad_to_max_length=True,
+        truncation = True,
+        return_attention_mask=True,
+      )
+      
+      input_ids.append(encoded['input_ids'])
+      attention_masks.append(encoded['attention_mask'])
+
+  return np.array(input_ids),np.array(attention_masks)
+    
+# train_encodings = tokenizer(train_texts, truncation=True, padding=True)
+# test_encodings = tokenizer(test_texts, truncation=True, padding=True)
+
+
+
+    
+def get_news(input_text):
+  sentence_length = 110
+  train_input_ids,train_attention_masks = bert_encode(tokenizer,[input_text],sentence_length)
+
+  pred = m.predict([train_input_ids,train_attention_masks])
+  pred = np.round(pred)
+  pred = pred.flatten()
+
+  if pred == 1:
+      result = "Fake News"
+  else:
+      result = "True News"
+  return result
+
+tweet_input = gr.Textbox(label = "Enter the tweet")
+output = gr.Textbox(label="Result")
+
+descripcion = (
+  """
+  <center>
+  Demo of the Covid-Twitter Fake News Detection System from my thesis.
+  </center>
+  """
+)
+iface = gr.Interface(fn = get_news, 
+                     inputs = tweet_input, 
+                     outputs = output,
+                     title = 'Covid Fake News Detection System', 
+                     description=descripcion,
+                     examples=["CDC Recommends Mothers Stop Breastfeeding To Boost Vaccine Efficacy",
+                               "An article claiming that Bill Gates' vaccine would modify human DNA.",
+                               "In the first half of 2020 WHO coordinated the logistics &amp; shipped 😷More than 3M surgical masks 🧤More than 2M gloves 🧰More than 1M diagnostic kits 🥼More than 200K gowns 🛡️More than 100K face shields to 135 countries across the🌍🌎🌏. https://t.co/iz4YQkbSGM",
+                               "Many COVID-19 treatments may be associated with adverse skin reactions and should be considered in a differential diagnosis new report says. https://t.co/GLSeYX2VDq"])
+                     
+iface.launch()

requeriments.txt

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+tensorflow~=2.8
+transformers
+huggingface-hub