Update app.py

app.py

@@ -70,7 +70,9 @@ import base64
 
 #for PDF form filling
 from PyPDFForm import FormWrapper
-
+from huggingface_hub import create_branch, create_tag
+from huggingface_hub import login
+from huggingface_hub import HfApi
 import os
 
 import zipfile
@@ -102,12 +104,10 @@ os.environ["LANGFUSE_HOST"] = os.getenv("LANGFUSE_HOST")
 
 DB_USER = 'u852023448_redmindgpt'
 DB_PASSWORD = 'redmindGpt@123'
-DB_HOST = '217.21.88.10'
+DB_HOST = 'srv1110.hstgr.io'
 DB_NAME = 'u852023448_redmindgpt'
 
-from huggingface_hub import create_branch, create_tag
-from huggingface_hub import login
-from huggingface_hub import HfApi
+
 api= HfApi()
 token = os.getenv("HF_TOKEN")
 login(token=os.getenv("HF_TOKEN"))
@@ -117,7 +117,7 @@ langfuse_handler.auth_check()  # Optional: Checks if the authentication is succe
 
 nltk.download('punkt')
 
-open_api_key_token = os.getenv("OPEN_AI_API")
+open_api_key_token = os.getenv("OPENAI_API_KEY")
 
 os.environ['OPENAI_API_KEY'] = open_api_key_token
 pdf_path = "Input/Inbound.pdf"
@@ -136,13 +136,13 @@ inventory_date = datetime.today().strftime('%Y-%m-%d')
 apis = [
     # fetch warehouse ID
     {
-        "url": "http://193.203.162.39:81/nxt-wms/userWarehouse/fetchWarehouseForUserId?",
-        "params": {"query": warehouse_name, "userId": 164}
+        "url": "http://193.203.162.39:8383/nxt-wms/userWarehouse/fetchWarehouseForUserId?",
+        "params": {"query": '', "userId": 164}
     },
 
     # Stock summary based on warehouse id
     {
-        "url": "http://193.203.162.39:81/nxt-wms/transactionHistory/stockSummary?",
+        "url": "http://193.203.162.39:8383/nxt-wms/transactionHistory/stockSummary?",
         "params": {"branchId": 343, "onDate": inventory_date, "warehouseId": warehouse_id}
     }
 ]
@@ -396,6 +396,10 @@ def make_api_request(url, params):
     """Generic function to make API GET requests and return JSON data."""
     try:
         response = requests.get(url, params=params)
+        print("url:",url)
+        print("params:",params)
+        print("response:",response)
+        print(response.status_code)
         response.raise_for_status()  # Raises an HTTPError if the response was an error
         return response.json()  # Return the parsed JSON data
     except requests.exceptions.HTTPError as http_err:
@@ -414,15 +418,17 @@ def inventory_report(question):
         print(f"Warehouse: {warehouse_name}, Email: {user_email}, Question: {user_question}")
     else:
         return "warehouse name not found"
-
+    print(f"warehouse name: {warehouse_name}")
+    apis[0]["params"]["query"] = warehouse_name
+    print(apis[0]["params"])
     data = make_api_request(apis[0]["url"], apis[0]["params"])
-    print(data)
+    print("Warehouse data:",data)
     if data:
-        print(f"warehouse name: {warehouse_name}")
+        
         # Extracting the id for the warehouse with the name "WH"
-        warehouse_id = next((item['id'] for item in data['result'] if item['wareHouseId'] == warehouse_name), None)
+        warehouse_id = next((item['id'] for item in data['result'] if item['name'] == warehouse_name), None)
         print(f"warehouse_id:{warehouse_id}")
-        if (warehouse_id):         
+        if warehouse_id:         
             print("before api id")
             # Step 3: Update the placeholder with the actual warehouse_id
             for api in apis:
@@ -434,32 +440,41 @@ def inventory_report(question):
             print(apis)
             data1 = make_api_request(apis[1]["url"], apis[1]["params"])
             print(data1)
-            if (data1):
+            if data1:
                 headers = ["S.No", "Warehouse Code", "Warehouse Name", "Customer Code", "Customer Name", "Item Code", "Item Name",
                            "Currency", "EAN", "UOM", "Quantity", "Gross Weight", "Volume", "Total Value"]
                 table_data = []
         
-                # Check if 'content' exists and is a list
-                if data1['result'].get('content', []):
-                    for index, item in enumerate(data1['result'], start=1):
-                        row = [
-                            index,  # Serial number
-                            item['warehouse']['code'],
-                            item['warehouse']['name'],
-                            item['customer']['code'],
-                            item['customer']['name'],
-                            item['skuMaster']['code'],
-                            item['skuMaster']['name'],
-                            item['currency']['code'],
-                            item['eanUpc'],
-                            item['uom']['code'],
-                            item['totalQty'],
-                            item['grossWeight'],
-                            item['volume'],
-                            item['totalValue']
-                        ]
-                        table_data.append(row)
+                # Check if 'result' and 'content' exist and have valid types
+                if 'result' in data1 and isinstance(data1['result'], dict):
+                    result = data1['result']
+                    if 'content' in result and isinstance(result['content'], list):
+                        for index, item in enumerate(result['content'], start=1):
+                            try:
+                                row = [
+                                    index,  # Serial number
+                                    item['warehouse']['code'],
+                                    item['warehouse']['name'],
+                                    item['customer']['code'],
+                                    item['customer']['name'],
+                                    item['skuMaster']['code'],
+                                    item['skuMaster']['name'],
+                                    item['currency']['code'],
+                                    item['eanUpc'],
+                                    item['uom']['code'],
+                                    item['totalQty'],
+                                    item['grossWeight'],
+                                    item['volume'],
+                                    item['totalValue']
+                                ]
+                                table_data.append(row)
+                            except KeyError as e:
+                                print(f"Missing Key Error for item: {item} - {e}")
+                    else:
+                        print("Content is either missing or not a list:", result.get('content'))
                 else:
+                    print("Result key is missing or not a dictionary in data1:", data1.get('result'))
+
                     print("No data available in 'content'.")
                     return "There are no inventory details for the warehouse you have given."
             
@@ -1117,17 +1132,23 @@ def chat_with_excel_data_dataframe(question):
     if isinstance(response_dataframe, pd.DataFrame) == False:          
         
         print("The result is not a DataFrame.")
-        if ":" in response_dataframe:
-            isDataFrame = False
-            names_part = response_dataframe.split(":", 1)[1]  # Get everything after the colon and space
+        print(str.lower("No employee details") not in response_dataframe)
+        if str.lower("No employee details") not in str.lower(response_dataframe):
+            if ":" in response_dataframe: 
+            
+                print("The result is a list of names.")
+                isDataFrame = False
+                names_part = response_dataframe.split(":", 1)[1]  # Get everything after the colon and space
 
-            # Split the names by commas to create a list
-            names = names_part.split(",")
+                # Split the names by commas to create a list
+                names = names_part.split(",")
 
-            # Convert the list of names to a DataFrame
-            response_dataframe = pd.DataFrame(names, columns=["Result"])
+                # Convert the list of names to a DataFrame
+                response_dataframe = pd.DataFrame(names, columns=["Result"])
+        else:
+                print("The result is a message.")
+                return response_dataframe   
             
-        
     #handle large dataset
     response = handle_large_dataset(response_dataframe, create_document,isDataFrame) 
       
@@ -1261,7 +1282,7 @@ def create_file_HF(file_path,directory,document_created = False):
     repo_id = "Redmind/NewageNXTGPT_Repo_trial" 
     
     
-    if(document_created):
+    if document_created:
         directory = directory + "/" + "document"
         document_created = False
     #check if direcotry exists
@@ -1439,8 +1460,8 @@ with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
         sample_button = gr.Button("What are the details of ASN24091600002", elem_classes="gr-buttonbig")
         sample_button1 = gr.Button("What are the active warehouses available", elem_classes="gr-buttonbig")
         sample_button2 = gr.Button("Explain Pre-Receiving Yard Management", elem_classes="gr-buttonbig")
-        sample_button3 = gr.Button("can you generate a doughnut chart with item name and quantities for warehouse WH1000001", elem_classes="gr-buttonbig")
-        sample_button4 = gr.Button("Analyze item name & quantity for different customers in a stacked bar chart for the warehouse WH1000001 & send email to meetarun@gmail.com", elem_classes="gr-button")
+        sample_button3 = gr.Button("can you generate a doughnut chart with item name and quantities for warehouse DUBAIWARE", elem_classes="gr-buttonbig")
+        sample_button4 = gr.Button("Analyze item name & quantity for different customers in a stacked bar chart for the warehouse DUBAIWARE & send email to meetarun@gmail.com", elem_classes="gr-button")
 
     # Chatbot component
     with gr.Row():