Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the model and tokenizer (make sure your model is correctly loaded here)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Specify the model path
+model_name = "ipc_refined_approach_model"  # Replace with your actual model path or name
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+model = model.to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define your legal sections
+sections = ['465', '467', '395', '332','353']  # Example sections, modify as per your actual list
+
+# Streamlit UI setup
+st.title("Legal Case Section Prediction")
+
+# Get input text from user
+st.subheader("Enter the legal text to predict the sections it belongs to:")
+input_text = st.text_area("Input Text", height=250)
+
+# Prediction function
+def predict_text(text):
+    # Tokenize and encode input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    # Move inputs to the same device as the model
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+    
+    # Perform inference
+    model.eval()
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits  # Ensure logits are accessed correctly
+    
+    # Apply sigmoid to get probabilities
+    probs = torch.sigmoid(logits).detach().cpu().numpy()  # Move to CPU for processing
+    
+    # Convert probabilities to binary predictions (threshold 0.5)
+    predictions = {section: int(prob > 0.5) for section, prob in zip(sections, probs[0])}
+    
+    # Return the sections the case belongs to
+    sections_belongs_to = [section for section, pred in predictions.items() if pred == 1]
+    return sections_belongs_to
+
+# Show results if input text is provided
+if input_text:
+    st.subheader("Prediction Results")
+    
+    # Get predictions for the input text
+    predicted_sections = predict_text(input_text)
+    
+    # Show predictions
+    if predicted_sections:
+        st.write(f"This case belongs to Section(s): {', '.join(predicted_sections)}")
+    else:
+        st.write("This case does not belong to any known section.")
+else:
+    st.write("Please enter some text to predict the sections.")