Update app.py

app.py

@@ -1,60 +1,33 @@
 
 import gradio as gr
-import logging
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 import os
 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',
-    handlers=[
-        logging.FileHandler('app.log'),
-        logging.StreamHandler()
-    ]
-)
-logger = logging.getLogger(__name__)
-
 # Salesforce connection
 def get_salesforce_connection():
-    try:
-        logger.info("Attempting to connect to Salesforce")
-        sf = Salesforce(
-            username=os.getenv("SF_USERNAME"),
-            password=os.getenv("SF_PASSWORD"),
-            security_token=os.getenv("SF_SECURITY_TOKEN")
-        )
-        logger.info("Salesforce connection established successfully")
-        return sf
-    except Exception as e:
-        logger.error(f"Failed to connect to Salesforce: {str(e)}")
-        raise e
+    sf = Salesforce(
+        username=os.getenv("SF_USERNAME"),
+        password=os.getenv("SF_PASSWORD"),
+        security_token=os.getenv("SF_SECURITY_TOKEN")
+    )
+    return sf
 
 # Load Hugging Face token
 HF_TOKEN = os.getenv("HF_TOKEN")
-if not HF_TOKEN:
-    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:
-    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)
-    logger.info(f"Model and tokenizer loaded successfully from {MODEL_PATH}")
-except Exception as e:
-    logger.error(f"Failed to load model from {MODEL_PATH}: {str(e)}")
-    raise e
+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)
 
 # Define input model for FastAPI
 class ChecklistInput(BaseModel):
@@ -73,67 +46,52 @@ app = FastAPI()
 
 @app.post("/generate")
 async def generate_checklist(data: ChecklistInput):
-    try:
-        logger.info(f"API request to /generate 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}"
-        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:
-                sf = get_salesforce_connection()
-                existing_record = sf.Supervisor_AI_Coaching__c.get(data.record_id, default={
-                    'Name': '',
-                    'Supervisor_ID__c': None,
-                    'Project_ID__c': None,
-                    'Reflection_Log__c': '',
-                    'Download_Link__c': '',
-                    'Engagement_Score__c': 0,
-                    'KPI_Flag__c': False,
-                    'Daily_Checklist__c': '',
-                    'Suggested_Tips__c': ''
-                })
-                logger.info(f"Fetched record {data.record_id}: {existing_record}")
-                update_data = {
-                    'Daily_Checklist__c': checklist,
-                    'Suggested_Tips__c': tips,
-                    'Engagement_Score__c': existing_record.get('Engagement_Score__c', 0) + 10,
-                    'KPI_Flag__c': kpi_flag,
-                    'Supervisor_ID__c': data.supervisor_id if data.supervisor_id else existing_record.get('Supervisor_ID__c'),
-                    'Project_ID__c': data.project_id_sf if data.project_id_sf else existing_record.get('Project_ID__c'),
-                    'Reflection_Log__c': data.reflection_log if data.reflection_log else existing_record.get('Reflection_Log__c', ''),
-                    '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)
-                logger.info(f"Updated Salesforce record {data.record_id} with fields: {update_data}")
-            except Exception as sf_e:
-                logger.error(f"Failed to update Salesforce: {str(sf_e)}")
-
-        return {
-            "checklist": checklist,
-            "tips": tips,
-            "kpi_flag": kpi_flag
+    inputs = f"Role: {data.role} Project: {data.project_id} ({data.project_name}) Milestones: {data.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)
+    tips = "1. Prioritize safety checks\n2. Review milestones\n3. Log progress"
+    kpi_flag = "delay" in data.milestones.lower() or "behind" in data.milestones.lower()
+
+    if data.record_id:
+        sf = get_salesforce_connection()
+        existing_record = sf.Supervisor_AI_Coaching__c.get(data.record_id, default={
+            'Name': '',
+            'Supervisor_ID__c': None,
+            'Project_ID__c': None,
+            'Reflection_Log__c': '',
+            'Download_Link__c': '',
+            'Engagement_Score__c': 0,
+            'KPI_Flag__c': False,
+            'Daily_Checklist__c': '',
+            'Suggested_Tips__c': ''
+        })
+        update_data = {
+            'Daily_Checklist__c': checklist,
+            'Suggested_Tips__c': tips,
+            'Engagement_Score__c': existing_record.get('Engagement_Score__c', 0) + 10,
+            'KPI_Flag__c': kpi_flag,
+            'Supervisor_ID__c': data.supervisor_id if data.supervisor_id else existing_record.get('Supervisor_ID__c'),
+            'Project_ID__c': data.project_id_sf if data.project_id_sf else existing_record.get('Project_ID__c'),
+            'Reflection_Log__c': data.reflection_log if data.reflection_log else existing_record.get('Reflection_Log__c', ''),
+            'Download_Link__c': data.download_link if data.download_link else existing_record.get('Download_Link__c', '')
         }
-    except Exception as e:
-        logger.error(f"Error in /generate endpoint: {str(e)}")
-        return {"error": str(e)}
+        sf.Supervisor_AI_Coaching__c.update(data.record_id, update_data)
+
+    return {
+        "checklist": checklist,
+        "tips": tips,
+        "kpi_flag": kpi_flag
+    }
 
 # 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:
-            logger.warning(f"No records found for Project_ID__c: {project_id_sf}")
-            return "No records found for Project ID.", "", False, get_logs()
+            return "No records found for Project ID.", "", False
 
         output = "Supervisor_AI_Coaching__c Records:\n"
         for record in records:
@@ -150,16 +108,13 @@ def login_and_display(project_id_sf):
                 f"Download Link: {record['Download_Link__c'] or 'N/A'}\n"
                 f"{'-'*50}\n"
             )
-        logger.info(f"Retrieved {len(records)} records for Project_ID__c: {project_id_sf}")
-        return output, "", False, get_logs()
+        return output, "", False
     except Exception as e:
-        logger.error(f"Error querying Salesforce: {str(e)}")
-        return f"Error querying Salesforce: {str(e)}", "", False, get_logs()
+        return f"Error querying Salesforce: {str(e)}", "", False
 
 # 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': '',
@@ -172,15 +127,12 @@ def gradio_generate_checklist(record_id, role="Supervisor", project_id="Unknown"
             'Daily_Checklist__c': '',
             'Suggested_Tips__c': ''
         })
-        logger.info(f"Fetched record {record_id}: {existing_record}")
         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,
@@ -193,23 +145,11 @@ 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)
-        logger.info(f"Updated Salesforce record {record_id} with fields: {update_data}")
-
-        return checklist, tips, kpi_flag, get_logs()
-    except Exception as e:
-        logger.error(f"Error generating checklist: {str(e)}")
-        return f"Error: {str(e)}", "", False, get_logs()
+        status = f"Updated Salesforce record {record_id}"
 
-# Read logs
-def get_logs():
-    try:
-        with open('app.log', 'r') as f:
-            logs = f.read()
-            logger.info("Retrieved logs from app.log")
-            return logs
+        return checklist, tips, kpi_flag, status
     except Exception as e:
-        logger.error(f"Error reading logs: {str(e)}")
-        return "No logs available."
+        return f"Error: {str(e)}", "", False, ""
 
 # Define Gradio interface
 with gr.Blocks() as iface:
@@ -220,11 +160,10 @@ 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), login_logs]
+            outputs=[records_output, gr.Textbox(visible=False), gr.Checkbox(visible=False)]
         )
 
     with gr.Tab("Generate Checklist"):
@@ -232,7 +171,7 @@ with gr.Blocks() as iface:
         role = gr.Textbox(label="Role", value="Supervisor")
         project_id = gr.Textbox(label="Project ID", value="P001")
         project_name = gr.Textbox(label="Project Name", value="Building A")
-        milestones = gr.Textbox(label="Project ID (Salesforce Project__c ID, optional)", value="")
+        milestones = gr.Textbox(label="Milestones", value="Complete foundation by 5/15")
         supervisor_id = gr.Textbox(label="Supervisor ID (Salesforce User ID, optional)", value="")
         project_id_sf = gr.Textbox(label="Project ID (Salesforce Project__c ID, optional)", value="")
         reflection_log = gr.Textbox(label="Reflection Log (optional)", value="")
@@ -241,21 +180,15 @@ 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")
-        generate_logs = gr.Textbox(label="Logs", lines=5, interactive=False)
+        status_output = gr.Textbox(label="Salesforce Status")
         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, generate_logs]
+            outputs=[checklist_output, tips_output, kpi_flag_output, status_output]
         )
 
 # Mount FastAPI
 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)
-        logger.info("Gradio interface launched successfully")
-    except Exception as e:
-        logger.error(f"Failed to launch Gradio interface: {str(e)}")
-        raise e
+    iface.launch(server_name="0.0.0.0", server_port=7860, share=False)