Create app.py

app.py

@@ -0,0 +1,43 @@
+
+from flask import Flask, request, jsonify
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import logging
+
+app = Flask(__name__)
+logging.basicConfig(level=logging.INFO)
+
+try:
+    model = AutoModelForSeq2SeqLM.from_pretrained("./fine_tuned_model")
+    tokenizer = AutoTokenizer.from_pretrained("./fine_tuned_model")
+except Exception as e:
+    logging.error(f"Error loading model: {e}")
+    raise e
+
+@app.route("/generate", methods=["POST"])
+def generate_checklist():
+    try:
+        data = request.json
+        role = data.get("role", "Supervisor")
+        project_id = data.get("project_id", "Unknown")
+        project_name = data.get("project_name", "Unknown Project")
+        milestones = data.get("milestones", "No milestones provided")
+        
+        inputs = f"Role: {role} Project: {project_id} ({project_name}) Milestones: {milestones}"
+        input_ids = tokenizer(inputs, return_tensors="pt", max_length=128, truncation=True).input_ids
+        outputs = model.generate(input_ids, max_length=128, num_beams=4, early_stopping=True)
+        checklist = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Simple tip generation (can be enhanced with model)
+        tips = "1. Prioritize safety checks\n2. Review milestones\n3. Log progress"
+        
+        return jsonify({
+            "checklist": checklist,
+            "tips": tips
+        })
+    except Exception as e:
+        logging.error(f"Error in generate_checklist: {e}")
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8000)
+```