Create app.py

app.py

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+import streamlit as st
+import torch
+import numpy as np
+from transformers import AutoTokenizer
+from transformers import BertForSequenceClassification
+
+
+st.set_page_config(layout='wide', initial_sidebar_state='expanded')
+col1, col2= st.columns(2)
+
+with col1:
+    st.title("FireWatch")
+    st.markdown("PREDICT WHETHER HEAT SIGNATURES AROUND THE GLOBE ARE LIKELY TO BE FIRES!")
+    st.markdown("Traing Code at:")
+    st.markdown("https://colab.research.google.com/drive/1-IfOMJ-X8MKzwm3UjbJbK6RmhT7tk_ye?usp=sharing")
+    st.markdown("Try the Model Yourself at:")
+    st.markdown("https://colab.research.google.com/drive/1GmweeQrkzs0OXQ_KNZsWd1PQVRLCWDKi?usp=sharing")
+
+
+    table_html = """
+    <table style="border-collapse: collapse; width: 100%;">
+  <tr style="border: 1px solid orange;">
+    <th style="border: 1px solid orange; font-weight: bold;">Category</th>
+    <th style="border: 1px solid orange; font-weight: bold;">Latitude, Longitude, Brightness, FRP</th>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Likely</td>
+    <td style="border: 1px solid orange;">-26.76123, 147.15512, 393.02, 203.63</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Likely</td>
+    <td style="border: 1px solid orange;">-26.7598, 147.14514, 361.54, 79.4</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Unlikely</td>
+    <td style="border: 1px solid orange;">-25.70059, 149.48932, 313.9, 5.15</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Unlikely</td>
+    <td style="border: 1px solid orange;">-24.4318, 151.83102, 307.98, 8.79</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Unlikely</td>
+    <td style="border: 1px solid orange;">-23.21878, 148.91298, 314.08, 7.4</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Likely</td>
+    <td style="border: 1px solid orange;">7.87518, 19.9241, 316.32, 39.63</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Unlikely</td>
+    <td style="border: 1px solid orange;">-20.10942, 148.14326, 314.39, 8.8</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Unlikely</td>
+    <td style="border: 1px solid orange;">7.87772, 19.9048, 304.14, 13.43</td>
+  </tr>
+  <tr style="border: 1px solid orange;">
+    <td style="border: 1px solid orange;">Likely</td>
+    <td style="border: 1px solid yellow;">7.8879, 19.92571, 328.6, 77.78</td>
+  </tr>
+    </table>
+    """
+    
+    st.markdown(table_html, unsafe_allow_html=True)
+
+
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
+def load_model(show_spinner=True):
+    MODEL_PATH = "NimaKL/FireWatch_5k"
+    model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
+    return model
+
+
+
+token_id = []
+attention_masks = []
+def preprocessing(input_text, tokenizer):
+    '''
+                  Returns <class transformers.tokenization_utils_base.BatchEncoding> with the following fields:
+                    - input_ids: list of token ids
+                    - token_type_ids: list of token type ids
+                    - attention_mask: list of indices (0,1) specifying which tokens should considered by the model (return_attention_mask = True).
+    '''
+    return tokenizer.encode_plus(
+        input_text,
+        add_special_tokens = True,
+        max_length = 16,
+        pad_to_max_length = True,
+        return_attention_mask = True,
+        return_tensors = 'pt'
+            )
+    
+def predict(new_sentence):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    # We need Token IDs and Attention Mask for inference on the new sentence
+    test_ids = []
+    test_attention_mask = []
+    # Apply the tokenizer
+    encoding = preprocessing(new_sentence, tokenizer)
+    # Extract IDs and Attention Mask
+    test_ids.append(encoding['input_ids'])
+    test_attention_mask.append(encoding['attention_mask'])
+    test_ids = torch.cat(test_ids, dim = 0)
+    test_attention_mask = torch.cat(test_attention_mask, dim = 0)
+    # Forward pass, calculate logit predictions
+    with torch.no_grad():
+        output = model(test_ids.to(device), token_type_ids = None, attention_mask = test_attention_mask.to(device))
+        prediction = 'Likely' if np.argmax(output.logits.cpu().numpy()).flatten().item() == 1 else 'Unlikely'
+        pred = 'Predicted Class: '+ prediction
+        return pred      
+
+model = load_model()
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+with col2:
+    text = st.text_input('Enter Prediction Data in Correct Format "Latitude, Longtitude, Brightness, FRP".\nExample:  8.81064, -65.07661, 328.04, 18.76 \nPredition Data: ')
+    aButton = st.button('Predict') 
+
+    if text or aButton:
+        with st.spinner('Wait for it...'):
+            st.success(predict(text))
+