Update app.py

app.py

@@ -20,12 +20,8 @@ from PyPDF2 import PdfReader
 
 memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history")
 
-
-
 open_api_key_token = os.environ['OPEN_AI_API']
 
-open_weather_api_key = os.environ['OPEN_WEATHER_API']
-
 os.environ['OPENAI_API_KEY'] = open_api_key_token
 db_uri = 'mysql+mysqlconnector://redmindgen:51([email protected]:3306/collegedb'
 #db_uri = 'postgresql+psycopg2://postgres:[email protected]:5432/warehouse'
@@ -35,12 +31,10 @@ db = SQLDatabase.from_uri(db_uri)
 
 # LLM setup
 llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
-#llm = OpenAI(temperature=0, api_key= os.environ['OPEN_AI_API'], model_name='gpt-3.5-turbo')
 
 # Define the SQL query generation tool
 template_query_generation = """Based on the table schema below, write a SQL query that would answer the user's question:
 {schema}
-
 Question: {question}
 SQL Query:"""
 prompt_query_generation = ChatPromptTemplate.from_template(template_query_generation)
@@ -66,26 +60,60 @@ def database_tool(question):
     sql_query = generate_sql_query(question)
     return run_query(sql_query)
 
-# Define the weather data retrieval tool
-def get_weather_data(location="United Arab Emirates"):
-    api_key = open_weather_api_key
-    base_url = "http://api.openweathermap.org/data/2.5/weather?"
-
-    if location is None or location.strip() == "":
-        location = "United Arab Emirates"
-
-    complete_url = f"{base_url}q={location}&appid={api_key}&units=metric"
-
-    response = requests.get(complete_url)
-    data = response.json()
+# Define the ASN API data retrieval tool
+def get_ASN_data(asn_id):
+     
+    base_url = "http://193.203.162.39:9090/nxt-wms/trnHeader?"
+
+    if asn_id is None or asn_id.strip() == "":
+        asn_id = "ASN24070100015"
+
+    complete_url = f"{base_url}branchMaster.id=343&transactionUid={asn_id}&userId=164&transactionType=ASN"
+    try:
+        response = requests.get(complete_url)
+        data = response.json()
+        response.raise_for_status()  # Raises an HTTPError if the response was an error
+
+        if data["httpStatus"] != "200":
+            # Assuming the first content item and first party item are what we're interested in
+            content = data['result']['content'][0]
+            trnHeaderAsn = content['trnHeaderAsn']
+            party = content['party'][0]
+        
+            # Extracting the required information
+            transactionUid = trnHeaderAsn['transactionUid']
+            customerOrderNo = trnHeaderAsn.get('customerOrderNo', 'N/A')  # Using .get() for potentially missing keys
+            orderDate = trnHeaderAsn.get('orderDate', 'N/A')
+            customerInvoiceNo = trnHeaderAsn.get('customerInvoiceNo', 'N/A')
+            invoiceDate = trnHeaderAsn.get('invoiceDate', 'N/A')
+            expectedReceivingDate = trnHeaderAsn['expectedReceivingDate']
+            transactionStatus = trnHeaderAsn['transactionStatus']
+            shipper_code = party['shipper']['code'] if party['shipper'] else 'N/A'
+            shipper_name = party['shipper']['name'] if party['shipper'] else 'N/A'
+
+            # Assuming the variables are already defined as per previous context
+            data = [
+                ["Transaction UID", transactionUid],
+                ["Customer Order No", customerOrderNo],
+                ["Order Date", orderDate],
+                ["Customer Invoice No", customerInvoiceNo],
+                ["Invoice Date", invoiceDate],
+                ["Expected Receiving Date", expectedReceivingDate],
+                ["Transaction Status", transactionStatus],
+                ["Shipper Code", shipper_code],
+                ["Shipper Name", shipper_name]
+            ]
+            return f"The ASN details of {asn_id} is {data}."
+        else:
+            return "ASN Details are not found. Please contact system administrator."
+        
+    except requests.exceptions.HTTPError as http_err:
+        print(f"HTTP error occurred: {http_err}")
+    except Exception as err:
+        print(f"An error occurred: {err}")
+    
 
-    if data["cod"] != "404":
-        main = data["main"]
-        weather_description = data["weather"][0]["description"]
-        temperature = main["temp"]
-        return f"The current temperature in {location} is {temperature}°C with {weather_description}."
-    else:
-        return "Weather data is not found."
+    
 #get_weather_data("United Arab Emirates")
 
 # Define the document data tool
@@ -109,11 +137,7 @@ def query_vector_store(vector_store, query):
     return '\n\n'.join([doc.page_content for doc in docs])
 
 # Load and process the PDF (ensure the PDF is accessible from your Colab environment)
-#pdf_path = "The Magic of Analysing Customers Experience in Freight Forwarding Industry -BLOG.pdf"
 pdf_path = "Inbound.pdf"
-# Check if the user has the necessary permissions to access the directory
-# if not os.path.isdir(pdf_path):
-#     raise ValueError(f"Directory {pdf_path} does not exist or you do not have permission to access it.")
 
 texts = load_and_split_pdf(pdf_path)
 vector_store = create_vector_store(texts)
@@ -124,29 +148,29 @@ def document_data_tool(query):
 # Initialize the agent with the tools
 tools = [
     
-    Tool(name="WeatherData", func=get_weather_data, description="Tool to get current weather data for a city or country. Handles questions related to current weather conditions in specific cities or countries.", tool_choice="required"),
+    Tool(name="ASNData", func=get_ASN_data, description="Tool to get the status of ASN with ASN id given as input. Handles questions related to ASN id which starts with ASN followed by 11 numeric digits. For example, ASN24070100015 ", tool_choice="required"),
     Tool(name="DocumentData", func=document_data_tool, description="Tool to search and retrieve information from the uploaded document. Provide responses with the maximum of 150 words.", tool_choice="required"),
     #Tool(name="DatabaseQuery", func=database_tool, description="Tool to query the database based on the user's question. Only handles questions related to the collegedb schema, including tables such as buildings, classrooms, college, course, faculty, interns, person, section, student, and textbook. Ensure to use only the available fields in these tables.Provide responses with the maximum of 150 words.", tool_choice="required"),
 ]
 
 prompt_template = f"""You are an assistant that helps with database queries, weather information, and document retrieval.
 
-For weather-related questions, if the user specifies a city, provide the weather information for that city. If the user specifies a country or does not specify a city, provide the weather information for the specified country or the default location of 'United Arab Emirates'.
+For ASN-related questions, if the user specifies ASN id. Provide the information like ASN status, expected Receiving Date etc.
 For document-related questions, search and retrieve information from the uploaded document.
 
 {{agent_scratchpad}}
 Question: {{input}}
-
+{{memory.buffer}}
 """
-#{memory.buffer}
+
 #For SQL database-related questions, only use the fields available in the collegedb schema, which includes tables such as buildings, classrooms, college, course, faculty, interns, person, section, student, and textbook.
 prompt = ChatPromptTemplate.from_template(prompt_template)
 
 # Initialize the agent with memory
 llm_with_memory = llm.bind(memory=memory)
-#llm_with_memory = llm.bind()
+
 agent = create_tool_calling_agent(llm_with_memory, tools, prompt)
-#agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
 agent_executor = AgentExecutor(agent=agent, tools=tools, memory= memory, verbose=True)
 
 # Define the interface function
@@ -154,6 +178,17 @@ max_iterations = 5
 iterations = 0
 
 def answer_question(user_question, city="United Arab Emirates"):
+    """
+    This function takes a user question as input and generates a response using an agent executor.
+    It iterates until a valid response is generated or the maximum number of iterations is reached.
+
+    Parameters:
+    - user_question (str): The question asked by the user.
+    - city (str): The city to use for the agent executor. Default is "United Arab Emirates".
+
+    Returns:
+    - str: The generated response or an error message if no valid response is generated within the iteration limit.
+    """
     global iterations
     iterations = 0
 
@@ -175,7 +210,6 @@ def answer_question(user_question, city="United Arab Emirates"):
     # Print memory buffer for debugging
     print("Memory Buffer11:", memory.load_memory_variables({}))
 
-
     # Format the response text
     response_text = response_text.replace('\n', ' ').replace('  ', ' ').strip()