Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 import os
@@ -12,12 +11,27 @@ load_dotenv()
 
 # Salesforce connection
 def get_salesforce_connection():
-    sf = Salesforce(
-        username=os.getenv("onteddugeetha104@gmail.com"),
-        password=os.getenv("9032590825g@"),
-        security_token=os.getenv("tTPLQduw8wDpxdKOMJ3d9dM3o")
-    )
-    return sf
+    try:
+        username = os.getenv("SF_USERNAME")
+        password = os.getenv("SF_PASSWORD")
+        security_token = os.getenv("SF_SECURITY_TOKEN")
+        domain = os.getenv("SF_DOMAIN", "login")  # Default to production (login.salesforce.com)
+        
+        if not all([username, password, security_token]):
+            raise ValueError("Missing Salesforce credentials. Check SF_USERNAME, SF_PASSWORD, SF_SECURITY_TOKEN in .env")
+
+        sf = Salesforce(
+            username=username,
+            password=password,
+            security_token=security_token,
+            domain=domain
+        )
+        
+        # Test connection by fetching user info
+        sf.User.get(sf.user_id)
+        return sf
+    except Exception as e:
+        raise Exception(f"Failed to connect to Salesforce: {str(e)}")
 
 # Load Hugging Face token
 HF_TOKEN = os.getenv("HF_TOKEN")
@@ -26,8 +40,11 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 MODEL_PATH = "facebook/bart-large"  # Public model
 # MODEL_PATH = "your_actual_username/fine_tuned_bart_construction"  # Uncomment after uploading
 
-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)
+try:
+    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)
+except Exception as e:
+    raise Exception(f"Failed to load model: {str(e)}")
 
 # Define input model for FastAPI
 class ChecklistInput(BaseModel):
@@ -46,43 +63,46 @@ app = FastAPI()
 
 @app.post("/generate")
 async def generate_checklist(data: ChecklistInput):
-    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', '')
+    try:
+        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', '')
+            }
+            sf.Supervisor_AI_Coaching__c.update(data.record_id, update_data)
+
+        return {
+            "checklist": checklist,
+            "tips": tips,
+            "kpi_flag": kpi_flag
         }
-        sf.Supervisor_AI_Coaching__c.update(data.record_id, update_data)
-
-    return {
-        "checklist": checklist,
-        "tips": tips,
-        "kpi_flag": kpi_flag
-    }
+    except Exception as e:
+        return {"error": str(e)}
 
 # Login and display records
 def login_and_display(project_id_sf):
@@ -191,4 +211,7 @@ with gr.Blocks() as iface:
 iface.app = app
 
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    try:
+        iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    except Exception as e:
+        print(f"Failed to launch Gradio: {str(e)}")