Update app.py

app.py

@@ -1,3 +1,4 @@
+```python
 import gradio as gr
 import logging
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
@@ -6,12 +7,21 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 from simple_salesforce import Salesforce
 from dotenv import load_dotenv
+from datetime import datetime
 
 # Load environment variables
 load_dotenv()
 
 # Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler('app.log'),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
 
 # Salesforce connection
 def get_salesforce_connection():
@@ -21,28 +31,28 @@ def get_salesforce_connection():
             password=os.getenv("SF_PASSWORD"),
             security_token=os.getenv("SF_SECURITY_TOKEN")
         )
-        logging.info("Salesforce connection established successfully")
+        logger.info("Salesforce connection established successfully")
         return sf
     except Exception as e:
-        logging.error(f"Failed to connect to Salesforce: {e}")
+        logger.error(f"Failed to connect to Salesforce: {str(e)}")
         raise e
 
 # Load Hugging Face token
 HF_TOKEN = os.getenv("HF_TOKEN")
 if not HF_TOKEN:
-    logging.warning("HF_TOKEN not set. Using public models only.")
+    logger.warning("HF_TOKEN not set. Using public models only.")
 
 # Model configuration
 MODEL_PATH = "facebook/bart-large"  # Public model
 # MODEL_PATH = "your_actual_username/fine_tuned_bart_construction"  # Uncomment after uploading
 
 try:
-    logging.info(f"Attempting to load model from {MODEL_PATH}")
+    logger.info(f"Attempting to load model from {MODEL_PATH}")
     model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH, use_auth_token=HF_TOKEN if HF_TOKEN else None)
     tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_auth_token=HF_TOKEN if HF_TOKEN else None)
-    logging.info(f"Model and tokenizer loaded successfully from {MODEL_PATH}")
+    logger.info(f"Model and tokenizer loaded successfully from {MODEL_PATH}")
 except Exception as e:
-    logging.error(f"Failed to load model from {MODEL_PATH}: {e}")
+    logger.error(f"Failed to load model from {MODEL_PATH}: {str(e)}")
     raise e
 
 # Define input model for FastAPI
@@ -63,13 +73,15 @@ app = FastAPI()
 @app.post("/generate")
 async def generate_checklist(data: ChecklistInput):
     try:
+        logger.info(f"Generating checklist for project_id_sf: {data.project_id_sf}, record_id: {data.record_id}")
         inputs = f"Role: {data.role} Project: {data.project_id} ({data.project_name}) Milestones: {data.milestones}"
-        logging.info(f"Generating checklist for inputs: {inputs[:100]}...")
+        logger.info(f"Input for model: {inputs[:100]}...")
         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)
         tips = "1. Prioritize safety checks\n2. Review milestones\n3. Log progress"
         kpi_flag = "delay" in data.milestones.lower() or "behind" in data.milestones.lower()
+        logger.info(f"Generated checklist: {checklist[:50]}..., tips: {tips[:50]}..., KPI Flag: {kpi_flag}")
 
         if data.record_id:
             try:
@@ -96,9 +108,9 @@ async def generate_checklist(data: ChecklistInput):
                     'Download_Link__c': data.download_link if data.download_link else existing_record.get('Download_Link__c', '')
                 }
                 sf.Supervisor_AI_Coaching__c.update(data.record_id, update_data)
-                logging.info(f"Updated Salesforce record {data.record_id} with fields: {update_data}")
+                logger.info(f"Updated Salesforce record {data.record_id} with fields: {update_data}")
             except Exception as sf_e:
-                logging.error(f"Failed to update Salesforce: {sf_e}")
+                logger.error(f"Failed to update Salesforce: {str(sf_e)}")
 
         return {
             "checklist": checklist,
@@ -106,17 +118,20 @@ async def generate_checklist(data: ChecklistInput):
             "kpi_flag": kpi_flag
         }
     except Exception as e:
-        logging.error(f"Error generating checklist: {e}")
+        logger.error(f"Error generating checklist: {str(e)}")
         return {"error": str(e)}
 
 # Login and display records
 def login_and_display(project_id_sf):
     try:
+        logger.info(f"Login attempt with Project_ID__c: {project_id_sf}")
         sf = get_salesforce_connection()
         query = f"SELECT Id, Name, Supervisor_ID__c, Project_ID__c, Daily_Checklist__c, Suggested_Tips__c, Reflection_Log__c, Engagement_Score__c, KPI_Flag__c, Download_Link__c FROM Supervisor_AI_Coaching__c WHERE Project_ID__c = '{project_id_sf}'"
+        logger.info(f"Executing SOQL query: {query}")
         records = sf.query(query)["records"]
         if not records:
-            return "No records found for Project ID.", "", False, ""
+            logger.warning(f"No records found for Project_ID__c: {project_id_sf}")
+            return "No records found for Project ID.", "", False, get_logs()
 
         output = "Supervisor_AI_Coaching__c Records:\n"
         for record in records:
@@ -133,13 +148,16 @@ def login_and_display(project_id_sf):
                 f"Download Link: {record['Download_Link__c'] or 'N/A'}\n"
                 f"{'-'*50}\n"
             )
-        return output, "", False, ""
+        logger.info(f"Retrieved {len(records)} records for Project_ID__c: {project_id_sf}")
+        return output, "", False, get_logs()
     except Exception as e:
-        return f"Error querying Salesforce: {str(e)}", "", False, ""
+        logger.error(f"Error querying Salesforce: {str(e)}")
+        return f"Error querying Salesforce: {str(e)}", "", False, get_logs()
 
 # Generate checklist from record
 def gradio_generate_checklist(record_id, role="Supervisor", project_id="Unknown", project_name="Unknown Project", milestones="No milestones provided", supervisor_id="", project_id_sf="", reflection_log="", download_link=""):
     try:
+        logger.info(f"Generating checklist for record_id: {record_id}")
         sf = get_salesforce_connection()
         existing_record = sf.Supervisor_AI_Coaching__c.get(record_id, default={
             'Name': '',
@@ -153,11 +171,13 @@ def gradio_generate_checklist(record_id, role="Supervisor", project_id="Unknown"
             'Suggested_Tips__c': ''
         })
         inputs = f"Role: {role} Project: {project_id} ({project_name}) Milestones: {milestones}"
+        logger.info(f"Model input: {inputs[:100]}...")
         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)
         tips = "1. Prioritize safety checks\n2. Review milestones\n3. Log progress"
         kpi_flag = "delay" in milestones.lower() or "behind" in milestones.lower()
+        logger.info(f"Generated checklist: {checklist[:50]}..., tips: {tips[:50]}..., KPI Flag: {kpi_flag}")
 
         update_data = {
             'Daily_Checklist__c': checklist,
@@ -170,11 +190,21 @@ def gradio_generate_checklist(record_id, role="Supervisor", project_id="Unknown"
             'Download_Link__c': download_link if download_link else existing_record.get('Download_Link__c', '')
         }
         sf.Supervisor_AI_Coaching__c.update(record_id, update_data)
-        status = f"Updated Salesforce record {record_id} with fields: {update_data}"
+        logger.info(f"Updated Salesforce record {record_id} with fields: {update_data}")
 
-        return checklist, tips, kpi_flag, status
+        return checklist, tips, kpi_flag, get_logs()
     except Exception as e:
-        return f"Error: {str(e)}", "", False, ""
+        logger.error(f"Error generating checklist: {str(e)}")
+        return f"Error: {str(e)}", "", False, get_logs()
+
+# Read logs
+def get_logs():
+    try:
+        with open('app.log', 'r') as f:
+            return f.read()
+    except Exception as e:
+        logger.error(f"Error reading logs: {str(e)}")
+        return "No logs available."
 
 # Define Gradio interface
 with gr.Blocks() as iface:
@@ -185,10 +215,11 @@ with gr.Blocks() as iface:
         project_id_input = gr.Textbox(label="Project ID (Salesforce Project__c ID)", placeholder="Enter Project ID")
         login_button = gr.Button("Submit")
         records_output = gr.Textbox(label="Records", lines=10)
+        login_logs = gr.Textbox(label="Logs", lines=5, interactive=False)
         login_button.click(
             fn=login_and_display,
             inputs=project_id_input,
-            outputs=[records_output, gr.Textbox(visible=False), gr.Checkbox(visible=False), gr.Textbox(visible=False)]
+            outputs=[records_output, gr.Textbox(visible=False), gr.Checkbox(visible=False), login_logs]
         )
 
     with gr.Tab("Generate Checklist"):
@@ -205,11 +236,11 @@ with gr.Blocks() as iface:
         checklist_output = gr.Textbox(label="Checklist")
         tips_output = gr.Textbox(label="Tips")
         kpi_flag_output = gr.Checkbox(label="KPI Flag")
-        status_output = gr.Textbox(label="Salesforce Status")
+        generate_logs = gr.Textbox(label="Logs", lines=5, interactive=False)
         generate_button.click(
             fn=gradio_generate_checklist,
             inputs=[record_id, role, project_id, project_name, milestones, supervisor_id, project_id_sf, reflection_log, download_link],
-            outputs=[checklist_output, tips_output, kpi_flag_output, status_output]
+            outputs=[checklist_output, tips_output, kpi_flag_output, generate_logs]
         )
 
 # Mount FastAPI
@@ -217,8 +248,10 @@ iface.app = app
 
 if __name__ == "__main__":
     try:
+        logger.info("Starting Gradio application")
         iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
-        logging.info("Gradio interface launched successfully")
+        logger.info("Gradio interface launched successfully")
     except Exception as e:
-        logging.error(f"Failed to launch Gradio interface: {e}")
+        logger.error(f"Failed to launch Gradio interface: {str(e)}")
         raise e
+```