Create app.py

app.py

@@ -0,0 +1,131 @@
+import torch
+import gradio as gr
+import pandas as pd
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+import json
+from datetime import datetime
+
+class LegalAISystem:
+    def __init__(self):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.tokenizer = AutoTokenizer.from_pretrained('nlpaueb/legal-bert-base-uncased')
+        self.model = AutoModelForSequenceClassification.from_pretrained('nlpaueb/legal-bert-base-uncased')
+        self.model.to(self.device)
+        self.label_encoder = LabelEncoder()
+        self.case_history = []
+
+    def preprocess_data(self, text):
+        # Clean and normalize text
+        text = str(text).lower().strip()
+        # Add more preprocessing steps as needed
+        return text
+
+    def extract_features(self, text):
+        # Tokenize and prepare features
+        inputs = self.tokenizer(
+            text,
+            padding=True,
+            truncation=True,
+            max_length=512,
+            return_tensors="pt"
+        ).to(self.device)
+        return inputs
+
+    def predict_outcome(self, case_text):
+        # Preprocess input
+        processed_text = self.preprocess_data(case_text)
+
+        # Extract features
+        features = self.extract_features(processed_text)
+
+        # Make prediction
+        with torch.no_grad():
+            outputs = self.model(**features)
+            predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+        # Get prediction probabilities
+        probs = predictions.cpu().numpy()[0]
+
+        # Store in case history
+        self.case_history.append({
+            'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            'case_text': case_text,
+            'prediction_probs': probs.tolist()
+        })
+
+        return {
+            'Favorable': float(probs[1]),
+            'Unfavorable': float(probs[0])
+        }
+
+    def analyze_precedents(self, case_text):
+        # Implement similarity search for relevant precedents
+        # This is a simplified version
+        return ["Precedent 1: Smith v. Jones (2019)",
+                "Precedent 2: Brown v. State (2020)"]
+
+    def generate_report(self, case_text, prediction, precedents):
+        report = f"""
+        Legal Case Analysis Report
+        ========================
+        Date: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+
+        Case Summary:
+        {case_text[:500]}...
+
+        Prediction:
+        - Favorable Outcome: {prediction['Favorable']:.2%}
+        - Unfavorable Outcome: {prediction['Unfavorable']:.2%}
+
+        Relevant Precedents:
+        {chr(10).join(precedents)}
+
+        Note: This is an AI-generated analysis and should be reviewed by legal professionals.
+        """
+        return report
+
+def create_gradio_interface():
+    legal_ai = LegalAISystem()
+
+    def process_case(case_text):
+        # Analyze case
+        prediction = legal_ai.predict_outcome(case_text)
+        precedents = legal_ai.analyze_precedents(case_text)
+        report = legal_ai.generate_report(case_text, prediction, precedents)
+
+        # Create visualization data
+        prob_chart = {
+            "Favorable": prediction['Favorable'],
+            "Unfavorable": prediction['Unfavorable']
+        }
+
+        return (
+            report,
+            prob_chart,
+            f"Confidence: {max(prediction.values()):.2%}"
+        )
+
+    # Create Gradio interface
+    iface = gr.Interface(
+        fn=process_case,
+        inputs=[
+            gr.Textbox(label="Enter Case Details", lines=10)
+        ],
+        outputs=[
+            gr.Textbox(label="Analysis Report", lines=10),
+            gr.Label(label="Outcome Probabilities"),
+            gr.Textbox(label="Model Confidence")
+        ],
+        title="AI Legal Case Analysis System",
+        description="Enter case details to get real-time analysis and predictions."
+    )
+
+    return iface
+
+# Launch the interface
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch(share=True, debug=True)