Clean up code and remove debug prints

app.py

@@ -1,4 +1,4 @@
-from flask import Flask, request, render_template
+from flask import Flask, request, render_template, send_file
 from transformers import AutoModelForTokenClassification, RobertaTokenizer, pipeline
 from collections import Counter
 import datetime, json
@@ -16,6 +16,7 @@ except Exception as e:
     print(f"Error loading pipeline: {str(e)}")
     raise
 
+# Main route
 @app.route("/", methods=["GET", "POST"])
 def index():
     predictions = []
@@ -35,9 +36,17 @@ def index():
             "input": input_text,
             "predictions": predictions
         }
+
+        # Save and print log in the log tab
         with open("log.jsonl", "a") as f:
             f.write(json.dumps(log_entry, default=str) + "\n")
 
+        print("\n--- LOG FILE CONTENTS ---")
+        with open("log.jsonl", "r") as f:
+            for line in f:
+                print(line.strip())
+        print("--- END OF LOG ---\n")
+
         label_counter = Counter([item["entity_group"] for item in predictions])
         tag_labels = list(label_counter.keys())
         tag_counts = list(label_counter.values())
@@ -45,6 +54,18 @@ def index():
     return render_template("index.html", predictions=predictions, input_text=input_text,
                            tag_labels=tag_labels, tag_counts=tag_counts)
 
+# Route to view log
+@app.route("/view_log")
+def view_log():
+    with open("log.jsonl", "r") as f:
+        contents = f.read()
+    return f"<pre>{contents}</pre>"
+
+# Route to download the log
+@app.route("/download_log")
+def download_log():
+    return send_file("log.jsonl", as_attachment=True)
+
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
     app.run(host="0.0.0.0", port=port)