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Update app.py
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app.py
CHANGED
@@ -135,18 +135,13 @@ def run_query(query):
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# The function that uses the above models
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# Define the function that will handle the database query
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def database_tool(question):
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# print(question)
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sql_query = generate_sql_query(question)
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#print(sql_query)
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return run_query(sql_query)
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def get_ASN_data(question):
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# print(question)
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base_url = os.getenv("ASN_API_URL")
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complete_url = f"{base_url}branchMaster.id=343&transactionUid={question}&userId=164&transactionType=ASN"
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#print("complete url")
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#print(complete_url)
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try:
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response = requests.get(complete_url)
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data = response.json()
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@@ -239,34 +234,21 @@ def document_data_tool(question):
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print(f"Document data tool enter: {question}")
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# query_string = question['tags'][0] if 'tags' in question and question['tags'] else ""
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query_response = query_vector_store(vector_store, question, config={"callbacks": [langfuse_handler]})
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#print("query****")
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#print(query_response)
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# summarized_response = summarize_document(query_response)
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# print("summary***")
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# print(summarized_response)
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return query_response
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def send_email_with_attachment(recipient_email, subject, body, image_data):
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try:
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print(recipient_email)
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print(subject)
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#print(image_data)
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sender_email = os.getenv("EMAIL_SENDER")
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sender_password = os.getenv("EMAIL_PASSWORD")
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print(sender_email)
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print(sender_password)
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# Create a multipart message
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msg = MIMEMultipart()
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msg['From'] = sender_email
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msg['To'] = recipient_email
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msg['Subject'] = subject
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print(body)
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# Attach the body with the msg instance
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msg.attach(MIMEText(body, 'plain'))
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print(body)
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"""
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# Open the file to be sent
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attachment = open(attachment_path, "rb")
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#print("Attached the image")
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@@ -283,45 +265,12 @@ def send_email_with_attachment(recipient_email, subject, body, image_data):
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# Attach the instance 'part' to instance 'msg'
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msg.attach(part)
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# Create a MIMEBase instance for the attachment
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part = MIMEBase('application', 'octet-stream')
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# Set the payload with the image data
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part.set_payload(image_data)
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# Encode the payload into base64
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encoders.encode_base64(part)
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# Add a header with the filename (you can set any filename you prefer)
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part.add_header('Content-Disposition', 'attachment; filename="image.png"')
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# Attach the instance 'part' to the instance 'msg'
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msg.attach(part)"""
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# Create SMTP session for sending the mail
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print(body)
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context = ssl.create_default_context()
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with smtplib.SMTP("smtp.gmail.com", 587) as server:
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server.ehlo() # Can be omitted
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server.starttls(context=context)
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server.ehlo() # Can be omitted
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server.login(sender_email, sender_password)
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text = msg.as_string()
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server.sendmail(sender_email, recipient_email, text)
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#server = smtplib.SMTP('smtp.gmail.com', 587)
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#server.starttls()
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#server.login(sender_email, sender_password)
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print(body)
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text = msg.as_string()
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print(body)
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server.sendmail(sender_email, recipient_email, text)
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print(body)
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server.quit()
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except Exception as error:
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@@ -361,12 +310,8 @@ def inventory_report(question):
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else:
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return "warehouse name not found"
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#print(apis[0]["url"])
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#print(apis[0]["params"])
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data = make_api_request(apis[0]["url"], apis[0]["params"])
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#print(warehouse_name)
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if data:
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#print(data)
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# Extracting the id for the warehouse with the name "WH"
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warehouse_id = next((item['id'] for item in data['result'] if item['name'] == warehouse_name), None)
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#print(warehouse_id)
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@@ -538,7 +483,7 @@ def answer_question(user_question, chatbot, audio=None):
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user_question = "Could not request results from Google Speech Recognition service."
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while iterations < max_iterations:
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"""if "send email to" in user_question:
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email_match = re.search(r"send email to ([\w\.-]+@[\w\.-]+)", user_question)
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if email_match:
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@@ -548,8 +493,7 @@ def answer_question(user_question, chatbot, audio=None):
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"""
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response = agent_executor.invoke({"input": user_question}, config={"callbacks": [langfuse_handler]})
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#print(warehouse_name)
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if isinstance(response, dict):
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response_text = response.get("output", "")
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else:
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@@ -557,10 +501,7 @@ def answer_question(user_question, chatbot, audio=None):
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if "invalid" not in response_text.lower():
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break
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iterations += 1
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#print(response_text)
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#print("validating. ")
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#print(os.getenv("IMAGE_PATH") in response_text)
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if iterations == max_iterations:
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return "The agent could not generate a valid response within the iteration limit."
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@@ -568,12 +509,12 @@ def answer_question(user_question, chatbot, audio=None):
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if os.getenv("IMAGE_PATH") in response_text:
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# Open the image file
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img = Image.open(os.getenv("IMAGE_PATH"))
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# Convert the PIL Image to a base64 encoded string
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buffered = BytesIO()
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img.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
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img = f'<img src="data:image/png;base64,{img_str}" style="width:500px; height:400px;">'
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# image = gr.Image(value=img_str)
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chatbot.append((user_question, img))
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@@ -582,19 +523,17 @@ def answer_question(user_question, chatbot, audio=None):
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match = re.search(email_pattern, user_question)
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if match:
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user_email = match.group() # Return the matched email
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#print(user_email)
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# email send
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if len(user_email)>0:
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# Send email with the chart image attached
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send_email_with_attachment(
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recipient_email=user_email,
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subject="Warehouse Inventory Report",
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body=response.get("output", "").split(":")[0],
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image_data = img_str
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# attachment_path=chart_path
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)
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if "send email to" in user_question:
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@@ -612,7 +551,6 @@ def answer_question(user_question, chatbot, audio=None):
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# return "/home/user/app/exports/charts/temp_chart.png"
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else:
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chatbot.append((user_question, response_text))
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# print(chatbot)
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return gr.update(value=chatbot)
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# response_text = response_text.replace('\n', ' ').replace(' ', ' ').strip()
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# return response_text
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@@ -709,7 +647,7 @@ def handle_dislike(data: gr.LikeData):
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return gr.update(visible=False), gr.update(visible=False)
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with gr.Blocks(css=css
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gr.Markdown("<CENTER><h2 style='font-size: 20px; font-family: Calibri;'>NewageNXT GPT</h2></CENTER>")
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with gr.Row():
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sample_button = gr.Button("What are the details of ASN24080600008", elem_classes="custom-button" , elem_id="button1")
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# The function that uses the above models
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# Define the function that will handle the database query
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def database_tool(question):
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sql_query = generate_sql_query(question)
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return run_query(sql_query)
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def get_ASN_data(question):
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base_url = os.getenv("ASN_API_URL")
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complete_url = f"{base_url}branchMaster.id=343&transactionUid={question}&userId=164&transactionType=ASN"
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try:
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response = requests.get(complete_url)
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data = response.json()
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print(f"Document data tool enter: {question}")
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# query_string = question['tags'][0] if 'tags' in question and question['tags'] else ""
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query_response = query_vector_store(vector_store, question, config={"callbacks": [langfuse_handler]})
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# summarized_response = summarize_document(query_response)
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return query_response
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def send_email_with_attachment(recipient_email, subject, body, image_data):
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try:
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sender_email = os.getenv("EMAIL_SENDER")
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sender_password = os.getenv("EMAIL_PASSWORD")
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# Create a multipart message
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msg = MIMEMultipart()
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msg['From'] = sender_email
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msg['To'] = recipient_email
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msg['Subject'] = subject
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# Attach the body with the msg instance
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msg.attach(MIMEText(body, 'plain'))
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# Open the file to be sent
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attachment = open(attachment_path, "rb")
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#print("Attached the image")
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# Attach the instance 'part' to instance 'msg'
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msg.attach(part)
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server = smtplib.SMTP('smtp.gmail.com', 587)
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server.starttls()
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server.login(sender_email, sender_password)
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text = msg.as_string()
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server.sendmail(sender_email, recipient_email, text)
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server.quit()
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except Exception as error:
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else:
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return "warehouse name not found"
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data = make_api_request(apis[0]["url"], apis[0]["params"])
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if data:
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# Extracting the id for the warehouse with the name "WH"
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warehouse_id = next((item['id'] for item in data['result'] if item['name'] == warehouse_name), None)
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#print(warehouse_id)
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user_question = "Could not request results from Google Speech Recognition service."
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while iterations < max_iterations:
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"""if "send email to" in user_question:
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email_match = re.search(r"send email to ([\w\.-]+@[\w\.-]+)", user_question)
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if email_match:
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"""
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response = agent_executor.invoke({"input": user_question}, config={"callbacks": [langfuse_handler]})
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if isinstance(response, dict):
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response_text = response.get("output", "")
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else:
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if "invalid" not in response_text.lower():
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break
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iterations += 1
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if iterations == max_iterations:
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return "The agent could not generate a valid response within the iteration limit."
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if os.getenv("IMAGE_PATH") in response_text:
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# Open the image file
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img = Image.open(os.getenv("IMAGE_PATH"))
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# Convert the PIL Image to a base64 encoded string
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buffered = BytesIO()
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img.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
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img = f'<img src="data:image/png;base64,{img_str}" style="width:500px; height:400px;">'
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# image = gr.Image(value=img_str)
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chatbot.append((user_question, img))
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match = re.search(email_pattern, user_question)
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if match:
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user_email = match.group() # Return the matched email
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# email send
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if len(user_email)>0:
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# Send email with the chart image attached
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send_email_with_attachment(
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recipient_email=user_email,
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subject="Warehouse Inventory Report",
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body=response.get("output", "").split(":")[0],
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# attachment_path=chart_path
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attachment_path=os.getenv("IMAGE_PATH")
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)
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if "send email to" in user_question:
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# return "/home/user/app/exports/charts/temp_chart.png"
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else:
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chatbot.append((user_question, response_text))
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return gr.update(value=chatbot)
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# response_text = response_text.replace('\n', ' ').replace(' ', ' ').strip()
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# return response_text
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return gr.update(visible=False), gr.update(visible=False)
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with gr.Blocks(css=css) as demo:
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gr.Markdown("<CENTER><h2 style='font-size: 20px; font-family: Calibri;'>NewageNXT GPT</h2></CENTER>")
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with gr.Row():
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sample_button = gr.Button("What are the details of ASN24080600008", elem_classes="custom-button" , elem_id="button1")
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