Update app.py

app.py

@@ -1,78 +1,97 @@
-import streamlit as st 
-import torch
-import re 
-import torch.nn as nn
-import joblib
-import torch.nn.functional as F
-
-st.title("News Classification")
-
-## mopdel 
-
-vocab_size = 37852
-embedding_dim = 45
-hidden_units = 25
-num_classes = 2
-max_len = 55
-
-class LSTMModel(nn.Module):
-    def __init__(self, vocab_size, embedding_dim, hidden_units, num_classes):
-        super(LSTMModel, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, embedding_dim)
-        self.lstm = nn.LSTM(embedding_dim, hidden_units, batch_first=True, dropout=0.2,bidirectional=True)
-        self.fc = nn.Linear(hidden_units* 2, num_classes)
-
-    def forward(self, x):
-        x = self.embedding(x)
-        output, _ = self.lstm(x)
-        x = output[:, -1, :]
-        x = self.fc(x)
-        return F.softmax(x, dim=1)
-
-model = LSTMModel(vocab_size, embedding_dim, hidden_units, num_classes)
-## load the weights
-model.load_state_dict(torch.load( "news_classfication.pth", map_location=torch.device("cpu")))
-model.eval()  
-
-tokenizer=joblib.load("tokenizer.pkl")
-
-def preprocess(words):
-    normalized = []
-    for i in words:
-        i = i.lower()
-        # get rid of urlss
-        i = re.sub('https?://\S+|www\.\S+', '', i)
-        # get rid of non words and extra spaces
-        i = re.sub('\\W', ' ', i)
-        i = re.sub('\n', '', i)
-        i = re.sub(' +', ' ', i)
-        i = re.sub('^ ', '', i)
-        i = re.sub(' $', '', i)
-
-        normalized.append(i)
-        text=[tokenizer.encode(text.lower()).ids for text in normalized]
-        max_length = 20 
-        flattened_text = [token for sublist in text for token in sublist]
-        if len(flattened_text) > max_length:
-            flattened_text = flattened_text[:max_length] 
-        else:
-            flattened_text += [0] * (max_length - len(flattened_text))
-        text_tensor = torch.tensor(flattened_text, dtype=torch.long)
-        text_tensor = text_tensor.unsqueeze(0)       
-    return text_tensor
-  
-
-
-
-text=st.text_input("Enter the news  Tittle ",value="Sheriff David Clarke Becomes An Internet Joke For Threatening To Poke People 'In The Eye'")
-
-if st.button("submit"):
-    words=text.split()
-    v=preprocess(words)
-    output=model(v)
-    if output.argmax()==0:
-        st.write("Its a Fake news")
-    else:
-        st.write("Its not a Fake news")
-
-
+import streamlit as st 
+import torch
+import re 
+import torch.nn as nn
+import joblib
+import torch.nn.functional as F
+
+
+
+## mopdel 
+
+vocab_size = 37852
+embedding_dim = 45
+hidden_units = 25
+num_classes = 2
+max_len = 55
+
+class LSTMModel(nn.Module):
+    def __init__(self, vocab_size, embedding_dim, hidden_units, num_classes):
+        super(LSTMModel, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, embedding_dim)
+        self.lstm = nn.LSTM(embedding_dim, hidden_units, batch_first=True, dropout=0.2,bidirectional=True)
+        self.fc = nn.Linear(hidden_units* 2, num_classes)
+
+    def forward(self, x):
+        x = self.embedding(x)
+        output, _ = self.lstm(x)
+        x = output[:, -1, :]
+        x = self.fc(x)
+        return F.softmax(x, dim=1)
+
+model = LSTMModel(vocab_size, embedding_dim, hidden_units, num_classes)
+## load the weights
+model.load_state_dict(torch.load( "news_classfication.pth", map_location=torch.device("cpu")))
+model.eval()  
+
+tokenizer=joblib.load("tokenizer.pkl")
+
+def preprocess(words):
+    normalized = []
+    for i in words:
+        i = i.lower()
+        # get rid of urlss
+        i = re.sub('https?://\S+|www\.\S+', '', i)
+        # get rid of non words and extra spaces
+        i = re.sub('\\W', ' ', i)
+        i = re.sub('\n', '', i)
+        i = re.sub(' +', ' ', i)
+        i = re.sub('^ ', '', i)
+        i = re.sub(' $', '', i)
+
+        normalized.append(i)
+        text=[tokenizer.encode(text.lower()).ids for text in normalized]
+        max_length = 20 
+        flattened_text = [token for sublist in text for token in sublist]
+        if len(flattened_text) > max_length:
+            flattened_text = flattened_text[:max_length] 
+        else:
+            flattened_text += [0] * (max_length - len(flattened_text))
+        text_tensor = torch.tensor(flattened_text, dtype=torch.long)
+        text_tensor = text_tensor.unsqueeze(0)       
+    return text_tensor
+  
+# 🖼 Streamlit UI
+st.set_page_config(page_title="Fake News Detector", page_icon="📰")
+st.title("📰 Fake News Detector")
+url = "https://tse1.mm.bing.net/th?id=OIP.P_-960Qckr5FUEU3KvjCMwHaEc&pid=Api&rs=1&c=1&qlt=95&w=208&h=124"
+st.image(url, width=400) 
+
+st.markdown(f"""
+    <style>
+        /* Set the background image for the entire app */
+        .stApp {{
+            background-color:#add8e6;
+            background-size: 100px;
+            background-repeat:no;
+            background-attachment: auto;
+            background-position:full;
+        }}
+    </style>
+    """, unsafe_allow_html=True)
+
+user_input = st.text_area("Enter News Text:", value="Sheriff David Clarke Becomes An Internet Joke For Threatening To Poke People 'In The Eye'",height=100)
+
+
+if st.button("submit"):
+    words=text.split()
+    v=preprocess(words)
+    output=model(v)
+    if output.argmax() == 0:
+        st.write("🚨 This is *Fake News*.")
+    else:
+        st.write("✅ This is *Not Fake News*.")
+
+
+
+