Upload 2 files

app.py

@@ -0,0 +1,119 @@
+import streamlit as st
+import torch
+import torch.nn.functional as F
+from transformers import BertTokenizer, BertModel
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+
+# Define the model class (matching the saved architecture)
+class HeadlineClassifier(torch.nn.Module):
+    def __init__(self, num_aspect_classes, num_polarity_classes):
+        super(HeadlineClassifier, self).__init__()
+        self.bert = BertModel.from_pretrained("sagorsarker/bangla-bert-base", return_dict=False)
+        self.drop = torch.nn.Dropout(0.5)
+        self.aspect_out = torch.nn.Linear(self.bert.config.hidden_size, num_aspect_classes)
+        self.polarity_out = torch.nn.Linear(self.bert.config.hidden_size, num_polarity_classes)
+
+    def forward(self, input_ids, attention_mask):
+        _, pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, return_dict=False)
+        output = self.drop(pooled_output)
+        aspect_output = self.aspect_out(output)
+        polarity_output = self.polarity_out(output)
+        return aspect_output, polarity_output
+
+# Load tokenizer and model
+tokenizer = BertTokenizer.from_pretrained("sagorsarker/bangla-bert-base")
+model = HeadlineClassifier(num_aspect_classes=4, num_polarity_classes=3)
+model.load_state_dict(torch.load('best_model_state1.bin', map_location=torch.device('cpu')))
+model.eval()
+
+# Class labels
+aspect_class_names = ["others", "politics", "religion", "sports"]
+polarity_class_names = ["negative", "neutral", "positive"]
+
+# Function for single text prediction
+def predict_text(text):
+    encoded = tokenizer.encode_plus(
+        text,
+        max_length=40,
+        add_special_tokens=True,
+        return_token_type_ids=False,
+        pad_to_max_length=True,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+    input_ids = encoded['input_ids']
+    attention_mask = encoded['attention_mask']
+
+    with torch.no_grad():
+        aspect_output, polarity_output = model(input_ids, attention_mask)
+        aspect_prediction = torch.argmax(aspect_output, dim=1).item()
+        polarity_prediction = torch.argmax(polarity_output, dim=1).item()
+
+    return aspect_class_names[aspect_prediction], polarity_class_names[polarity_prediction]
+
+# Function to scrape headlines with multiple classes
+def scrape_headlines(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, "html.parser")
+    
+    # Extract headlines with the specified classes
+    headlines = [h.get_text(strip=True) for h in soup.find_all("a", class_=["title-link", "stretched-link", "Title"])[:50]]
+    return headlines
+
+# Streamlit App Interface
+st.title("Bangla Headline Aspect and Polarity Predictor")
+
+# Radio button for functionality selection
+option = st.radio("Choose Analysis Type:", ("Particular", "Overall"))
+
+if option == "Particular":
+    # Input for single text prediction
+    text_input = st.text_area("Enter your Bangla text:")
+    if st.button("Predict"):
+        if text_input.strip():
+            aspect, polarity = predict_text(text_input)
+            st.write("### Original Text:")
+            st.write(f"{text_input}")
+            st.write(f"**Predicted Aspect Class:** {aspect}")
+            st.write(f"**Predicted Polarity Class:** {polarity}")
+        else:
+            st.warning("Please enter some text to predict.")
+
+elif option == "Overall":
+    # Input for URL and headline analysis
+    url_input = st.text_input("Enter the URL:")
+    if st.button("Analyze Headlines"):
+        if url_input.strip():
+            headlines = scrape_headlines(url_input)
+            if not headlines:
+                st.warning("No headlines found. Please check the URL or structure of the site.")
+            else:
+                # Initialize counters
+                aspect_counts = {cls: 0 for cls in aspect_class_names}
+                polarity_counts = {cls: 0 for cls in polarity_class_names}
+                
+                # Process each headline
+                for headline in headlines:
+                    aspect, polarity = predict_text(headline)
+                    aspect_counts[aspect] += 1
+                    polarity_counts[polarity] += 1
+
+                # Display counts
+                st.write("### Aspect Class Counts")
+                for cls in aspect_class_names:
+                    st.write(f"{cls}: {aspect_counts[cls]}")
+
+                st.write("### Polarity Class Counts")
+                for cls in polarity_class_names:
+                    st.write(f"{cls}: {polarity_counts[cls]}")
+                
+                # Display bar charts
+                st.write("### Aspect Distribution")
+                st.bar_chart(pd.DataFrame(list(aspect_counts.items()), columns=['Aspect', 'Count']).set_index('Aspect'))
+                
+                st.write("### Polarity Distribution")
+                st.bar_chart(pd.DataFrame(list(polarity_counts.items()), columns=['Polarity', 'Count']).set_index('Polarity'))
+        else:
+            st.warning("Please enter a valid URL.")

best_model_state1.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:359a527621945876ad41c0acffc3ba4980bec1839cdd52ca883790d9675543e4
+size 657692018