Update app.py

app.py

@@ -9,9 +9,26 @@ from PIL import Image
 import base64
 from io import BytesIO
 import os
+import re
+import tempfile
+import wave
 import requests
 import gradio as gr
-#import nltk
+import time
+import shutil
+import json
+import nltk
+#audio package
+import speech_recognition as sr
+from pydub import AudioSegment
+from pydub.playback import play
+#email library
+import smtplib
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
+#langchain
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnableSequence, RunnableLambda
@@ -27,21 +44,30 @@ from PyPDF2 import PdfReader
 from nltk.tokenize import sent_tokenize
 from sqlalchemy import create_engine
 from sqlalchemy.sql import text
-import json
-import nltk
+
+#pandas
+import pandas as pd
+from pandasai.llm.openai import OpenAI
+from pandasai import SmartDataframe
+
+
+
+
 nltk.download('punkt')
 
 open_api_key_token = os.environ['OPEN_AI_API']
 
 os.environ['OPENAI_API_KEY'] = open_api_key_token
+pdf_path="Inbound.pdf"
+
 db_uri = 'postgresql+psycopg2://postgres:[email protected]:5432/warehouseAi'
 # Database setup
 
 db = SQLDatabase.from_uri(db_uri)
 
 # LLM setup
-#llm = ChatOpenAI(model="gpt-3.5-turbo-0125",max_tokens=150,temperature=0.1)
-llm = ChatOpenAI(model="gpt-4o-mini",max_tokens=250,temperature=0.1)
+llm = ChatOpenAI(model="gpt-4o-mini",max_tokens=300,temperature=0.1)
+llm_chart = OpenAI()
 
 def get_schema(_):
     schema_info = db.get_table_info()  # This should be a string of your SQL schema
@@ -69,7 +95,7 @@ def generate_sql_query(question):
 def run_query(query):
     # Clean the query by removing markdown symbols and trimming whitespace
     clean_query = query.replace("```sql", "").replace("```", "").strip()
-    #print(f"Executing SQL Query: {clean_query}")
+    print(f"Executing SQL Query: {clean_query}")
     try:
         result = db.run(clean_query)
         return result
@@ -83,7 +109,7 @@ def run_query(query):
 def database_tool(question):
     # print(question)
     sql_query = generate_sql_query(question)
-    ##print(sql_query)
+    print(sql_query)
     return run_query(sql_query)
 
 def get_ASN_data(question):
@@ -149,7 +175,7 @@ def create_vector_store(texts):
 
 def query_vector_store(vector_store, query):
     docs = vector_store.similarity_search(query, k=5)
-    #print(f"Vector store return: {docs}")
+    print(f"Vector store return: {docs}")
     return docs
 
 def summarize_document(docs):
@@ -167,13 +193,12 @@ def summarize_document(docs):
             summarized_content = doc_content
         summarized_docs.append(summarized_content)
     return '\n\n'.join(summarized_docs)
-pdf_path = "Inbound.pdf"
-#pdf_path = r"D:\rajesh\python\chat_agent\Inbound.pdf"
+
 texts = load_and_split_pdf(pdf_path)
 vector_store = create_vector_store(texts)
 
 def document_data_tool(question):
-    #print(f"Document data tool enter: {question}")
+    print(f"Document data tool enter: {question}")
     # query_string = question['tags'][0] if 'tags' in question and question['tags'] else ""
     query_response = query_vector_store(vector_store, question)
     print("query****")
@@ -182,6 +207,45 @@ def document_data_tool(question):
     #print("summary***")
     #print(summarized_response)
     return query_response
+    
+def send_email_with_attachment(recipient_email, subject, body, attachment_path):
+    sender_email = "[email protected]"
+    sender_password = "jymz apyc raih eubg"
+
+    # Create a multipart message
+    msg = MIMEMultipart()
+    msg['From'] = sender_email
+    msg['To'] = recipient_email
+    msg['Subject'] = subject
+
+    # Attach the body with the msg instance
+    msg.attach(MIMEText(body, 'plain'))
+
+    # Open the file to be sent
+    attachment = open(attachment_path, "rb")
+
+    # Instance of MIMEBase and named as p
+    part = MIMEBase('application', 'octet-stream')
+
+    # To change the payload into encoded form
+    part.set_payload((attachment).read())
+
+    # Encode into base64
+    encoders.encode_base64(part)
+
+    part.add_header('Content-Disposition', f"attachment; filename= {attachment_path}")
+
+    # Attach the instance 'part' to instance 'msg'
+    msg.attach(part)
+
+    # Create SMTP session for sending the mail
+    server = smtplib.SMTP('smtp.gmail.com', 587)
+    server.starttls()
+    server.login(sender_email, sender_password)
+    text = msg.as_string()
+    server.sendmail(sender_email, recipient_email, text)
+    server.quit()
+    #return 1
 
 def make_api_request(url, params):
   import requests
@@ -203,26 +267,23 @@ apis = [
         "url": "http://193.203.162.39:9090/nxt-wms/userWarehouse/fetchWarehouseForUserId?",
         "params": {"query": name, "userId": "164"}
     },
-         #fetch customer id
-     {
-        "url": "http://193.203.162.39:9090/nxt-wms/userCustomer/fetchCustomerForUserId?",
-        "params": {"query": "TESTING 123", "userId": "164", "status": "Active"}
-    },
+        
          #Stock summary based on warehouse id
     {
         "url": "http://193.203.162.39:9090/nxt-wms/transactionHistory/stockSummary?",
-        "params": {"branchId": "343", "onDate": "2024-08-06", "warehouseId" : warehouse_id }
+        "params": {"branchId": "343", "onDate": "2024-08-09", "warehouseId" : warehouse_id }
     }
 ]
 
 def inventory_report(question):
 
+  # Split the question to extract warehouse name, user question, and optional email
+  parts = question.split(":", 2)
+  name = parts[0].strip()
+  user_question = parts[1].strip()
+  user_email = parts[2].strip() if len(parts) > 2 else None
+  print(f"Warehouse: {name}, Email: {user_email}, Question: {user_question}")
 
-  name = question.split(":")[0]
-  #print(question)
-  question = question.split(":")[1]
-  #print(name)
-  import requests
 
   data = make_api_request(apis[0]["url"], apis[0]["params"])
   if data:
@@ -236,11 +297,8 @@ def inventory_report(question):
         if "warehouseId" in api["params"]:
           api["params"]["warehouseId"] = warehouse_id
 
-  #print(apis[2]["url"])
-  #print(apis[2]["params"])
-  data1 = make_api_request(apis[2]["url"], apis[2]["params"])
-  #if data1:
-    #print(data1)
+
+  data1 = make_api_request(apis[1]["url"], apis[1]["params"])
 
   from tabulate import tabulate
 
@@ -268,22 +326,25 @@ def inventory_report(question):
     table_data.append(row)
 
 
-  #if table_data:
-      #print(tabulate(table_data, headers=headers, tablefmt="grid"))
-
-      # Convert to pandas DataFrame
-  import pandas as pd
+ # Convert to pandas DataFrame
   df = pd.DataFrame(table_data, columns=headers)
-  import pandas as pd
-  from pandasai.llm.openai import OpenAI
-  from pandasai import SmartDataframe
-  #open api key
-  import openai
- 
-  llm = OpenAI()
-  sdf = SmartDataframe(df, config={"llm": llm})
+
+  sdf = SmartDataframe(df, config={"llm": llm_chart})
+
   #chart = sdf.chat("Can you draw a bar chart with all avaialble item name and quantity.")
   chart = sdf.chat(question)
+
+  #email send
+  if user_email:
+      # Send email with the chart image attached
+    send_email_with_attachment(
+        recipient_email=user_email,
+        subject="Warehouse Inventory Report",
+        body="Please find the attached bar chart report for the warehouse inventory analysis.",
+        #attachment_path=chart_path
+        attachment_path="/home/user/app/exports/charts/temp_chart.png"
+    )
+
   return chart
 #inventory_report("WH:can you give me a bar chart with item name and quantity for the warehouse WH")
 
@@ -325,7 +386,19 @@ tools = [
         name="dataVisualization",
         args_schema=QueryInput,
         output_schema=QueryOutput,
-        description="Tool to generate visual output for a particular warehouse. Invoke this tool if the user wants to create charts. Process the user question and send two inputs to the tool. One input will be the warehouse name and another input to the tool will be the entire user_question itself. "
+        description = """
+        Tool to generate a visual output (such as a bar chart) for a particular warehouse based on the provided question.
+        This tool processes the user question to identify the warehouse name and the specific request. If the user specifies
+        an email, include the email in the input. The input format should be: 'warehouse name: user question: email (if any)'.
+        The tool generates the requested chart and sends it to the provided email if specified.
+
+        Examples:
+        1. Question without email: "Analyze item name and quantity in a bar chart in warehouse Allcargo Logistics"
+          Input to tool: "Allcargo Logistics: I want to analyze item name and quantity in a bar chart"
+
+        2. Question with email: "Analyze item name and quantity in a bar chart in warehouse Allcargo Logistics report to send email to [email protected]"
+          Input to tool: "Allcargo Logistics: I want to analyze item name and quantity in a bar chart: [email protected]"
+        """
     )
 ]
 
@@ -342,25 +415,71 @@ llm = llm.bind()
 agent = create_tool_calling_agent(llm, tools, ChatPromptTemplate.from_template(prompt_template))
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
+def ensure_temp_chart_dir():
+    temp_chart_dir = "/home/user/app/exports/charts/"
+    if not os.path.exists(temp_chart_dir):
+        os.makedirs(temp_chart_dir)
+
+def clean_gradio_tmp_dir():
+    tmp_dir = "/tmp/gradio/"
+    if os.path.exists(tmp_dir):
+        try:
+            shutil.rmtree(tmp_dir)
+        except Exception as e:
+            print(f"Error cleaning up /tmp/gradio/ directory: {e}")
+
 # Define the interface function
 max_iterations = 5
 iterations = 0
 
-def answer_question(user_question,chatbot):
+def answer_question(user_question, chatbot, audio=None):
     global iterations
     iterations = 0
+    # Ensure the temporary chart directory exists
+    #ensure_temp_chart_dir()
+    # Clean the /tmp/gradio/ directory
+    #clean_gradio_tmp_dir()
+    # Handle audio input if provided
+    if audio is not None:
+        sample_rate, audio_data = audio
+        audio_segment = AudioSegment(
+            audio_data.tobytes(),
+            frame_rate=sample_rate,
+            sample_width=audio_data.dtype.itemsize,
+            channels=1
+        )
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
+            audio_segment.export(temp_audio_file.name, format="wav")
+            temp_audio_file_path = temp_audio_file.name
+
+        recognizer = sr.Recognizer()
+        with sr.AudioFile(temp_audio_file_path) as source:
+            audio_content = recognizer.record(source)
+        try:
+            user_question = recognizer.recognize_google(audio_content)
+        except sr.UnknownValueError:
+            user_question = "Sorry, I could not understand the audio."
+        except sr.RequestError:
+            user_question = "Could not request results from Google Speech Recognition service."
 
     while iterations < max_iterations:
-      #print(user_question)
-      response = agent_executor.invoke({"input": user_question})
-      #print(response)
-      if isinstance(response, dict):
-          response_text = response.get("output", "")
-      else:
-          response_text = response
-      if "invalid" not in response_text.lower():
-          break
-      iterations += 1
+        print(user_question)
+        if "send email to" in user_question:
+          email_match = re.search(r"send email to ([\w\.-]+@[\w\.-]+)", user_question)
+          if email_match:
+            user_email = email_match.group(1).strip()
+            user_question = user_question.replace(f"send email to {user_email}", "").strip()
+            user_question = f"{user_question}:{user_email}"
+
+        response = agent_executor.invoke({"input": user_question})
+
+        if isinstance(response, dict):
+            response_text = response.get("output", "")
+        else:
+            response_text = response
+        if "invalid" not in response_text.lower():
+            break
+        iterations += 1
 
     if iterations == max_iterations:
         return "The agent could not generate a valid response within the iteration limit."
@@ -378,7 +497,18 @@ def answer_question(user_question,chatbot):
         #image = gr.Image(value=img_str)
         chatbot.append((user_question,img))
         #print(chatbot)
+		if "send email to" in user_question:
+                    try:
+                        os.remove(image_path)  # Clean up the temporary image file
+                    except Exception as e:
+                        print(f"Error cleaning up image file: {e}")
+            except Exception as e:
+                print(f"Error loading image file: {e}")
+                chatbot.append((user_question, "Chart generation failed. Please try again."))
+        else:
+            chatbot.append((user_question, "Chart generation failed. Please try again."))
         return gr.update(value=chatbot)
+        
 
       #return [(user_question,gr.Image("/home/user/app/exports/charts/temp_chart.png"))]
       #  return "/home/user/app/exports/charts/temp_chart.png"
@@ -432,6 +562,7 @@ with gr.Blocks(css=css) as demo:
     with gr.Row():
         with gr.Column(scale=1):
             message = gr.Textbox(show_label=False)
+            audio_input = gr.Audio(label="Record your question")
         with gr.Column(scale=1):
             with gr.Row():
                 button = gr.Button("Submit", elem_classes="gr-button")
@@ -458,3 +589,4 @@ with gr.Blocks(css=css) as demo:
    
     
 demo.launch()
+