Create app.py

app.py

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+import os
+import json
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+from flask import Flask, request, jsonify
+from pathlib import Path
+from datetime import datetime
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load pre-trained model and tokenizer
+MODEL_PATH = "path/to/your/model"  # Update with your Hugging Face model path
+tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
+model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
+
+# Function to process contract text and classify clauses
+def classify_clause(contract_text):
+    inputs = tokenizer(contract_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=-1).item()  # Get predicted class (risk tag)
+    
+    # Define risk labels (assuming 3 risk levels: low, medium, high)
+    risk_labels = ["low", "medium", "high"]
+    predicted_risk = risk_labels[predicted_class]
+
+    # Get confidence score (softmax output)
+    softmax = torch.nn.Softmax(dim=-1)
+    confidence = softmax(logits).squeeze().tolist()[predicted_class]
+
+    return {"predicted_risk": predicted_risk, "confidence_score": confidence}
+
+# Define route to handle file uploads
+@app.route("/upload_contract", methods=["POST"])
+def upload_contract():
+    # Extract file from the request
+    if 'file' not in request.files:
+        return jsonify({"error": "No file part"}), 400
+    
+    file = request.files['file']
+    
+    if file.filename == '':
+        return jsonify({"error": "No selected file"}), 400
+    
+    contract_text = file.read().decode('utf-8')  # Assuming the file is a text-based contract
+    
+    # Classify the contract text
+    result = classify_clause(contract_text)
+
+    # Prepare JSON response
+    response_data = {
+        "contract_title": "Sample Contract",  # Placeholder, can be parsed from the file
+        "overall_risk_score": result["predicted_risk"],  # Risk classification
+        "high_risk_clauses": ["Termination Clause", "Penalty Clause"],  # Example (this should be dynamically extracted)
+        "risk_map_url": "https://example.com/risk_map",  # Placeholder (use actual URL for visualization)
+        "evaluation_date": datetime.now().strftime("%Y-%m-%d")
+    }
+
+    # Return response as JSON
+    return jsonify(response_data)
+
+if __name__ == "__main__":
+    app.run(debug=True, host="0.0.0.0", port=5000)