import ast import requests import json from duckduckgo_search import DDGS import google.generativeai as genai from groq import Groq import time import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart import markdown import streamlit as st genai.configure(api_key='AIzaSyCootL_jwKI3YDb6cKRJV-Ad0N4oKlLXXE') client = Groq(api_key='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') # 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 def jina(url): base_url= "https://r.jina.ai//" url=base_url+url response=requests.get(url) return response.text def groq_inference(query): # client = Groq() completion = client.chat.completions.create( model="llama3-groq-70b-8192-tool-use-preview", messages=[ { "role": "user", "content": query } ], temperature=0, max_tokens=2040, top_p=0.65, # stream=True, stop=None, ) # for chunk in completion: # print(chunk.choices[0].delta.content or "", end="") # return completion.choices[0].delta.content return completion.choices[0].message.content #Know about product def serper_prod(company): url = "https://google.serper.dev/news" payload = json.dumps({ "q": f"{company} info", }) headers = { 'X-API-KEY': '7d6a39f71072f99cd421dbdd6cfebc73e2a66a07', 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) return json.loads(response.text) #Know about the competiton\competitiors def serper_compi(company): url = "https://google.serper.dev/search" payload = json.dumps({ "q": f"{company} competitors.", }) headers = { 'X-API-KEY': '7d6a39f71072f99cd421dbdd6cfebc73e2a66a07', 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) return json.loads(response.text) def AI_Search_compi(text): ans = DDGS().chat("Summarize the text and dont remove the important terms about products or applications which should be helped for planning market for a company " + text, model='claude-3-haiku') return ans def AI_Search_compi(text,titles,name): ans = groq_inference(f"""Summarize the text of the articles titles are {titles} and dont remove the important terms about products or applications which should be helped for knowing about the compititors for a company {name} and the data is: {text}""") return ans def AI_Product_Analysis(text): ans = DDGS().chat("Analyze the products mentioned in the following news article in 400 words or fewer. Focus on their features, market relevance, and potential impact for a company's market planning: " + text, model='claude-3-haiku') return ans def AI_Product_Summary(news_summaries,product): # combined_summaries = " ".join(news_summaries) ans = groq_inference(f"""Create a comprehensive summary of the product {product} based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost and remember that these are form the news articles so you may have add text also : """ + news_summaries) # ans = DDGS().chat("Create a comprehensive summary of the product based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost: " + news_summaries, model='gpt-4o-mini') return ans def AI_Product_Summary_prod(news_summaries): # combined_summaries = " ".join(news_summaries) ans = groq_inference(f"""Create a comprehensive summary of the product based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost and remember that these are form the news articles so you may have add text also : """ + news_summaries) # ans = DDGS().chat("Create a comprehensive summary of the product based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost: " + news_summaries, model='gpt-4o-mini') return ans # AI_Product_Summary_prod def AI_Search_extract_cmpy(text): # prompt = """You will be given a dynamic text that summarizes key products, applications, and comparisons from articles about VR headsets. Your task is to extract relevant product names from the text and generate a list of search queries suitable for a search API.Don't give more than five names in the list. # Output Format: # The output should be a list in the following format: # ['company - product name', 'company - product name', 'product name', ...] # If the company name is unknown, only include the product name without the company name. # Do not include any introductory or explanatory text in the output; provide only the list in brackets. # Input Format: # The input will be a summary text containing product names, features, and key points.""" #$#$#$#$# prompt = """You will be given a dynamic text that summarizes key products, applications, and comparisons from articles about VR headsets. Your task is to extract relevant product names from the text and generate a list of search queries suitable for a search API. Don't give more than five names in the list. Output Format: Provide only the list in the following format, without any explanatory or introductory text: ['company - product name', 'company - product name', 'product name', ...] and remember dont give like this ['McDonald's - Big Mac'] if the company is like this then give it as ['McDonalds - Big Mac'] or ['Dunkin's Donut'] to ['Dunkins Donut'] etc, becauses this will be given to pyhton this return error. If the company name is unknown, only include the product name without the company name.""" # prompt = """You will be given a dynamic text that summarizes key products, applications, and comparisons from articles about VR headsets. Your task is to extract relevant product names from the text and generate a list of search queries suitable for a search API. Don't give more than five names in the list.Dont repeat the company names and please dont add single quotes or any other special characters that will be given error for pyhton to read. # Output Format: # Provide only the list in the following format, without any explanatory or introductory text: # ['company - product name', 'company - product name', 'product name', ...] # and remember dont give like this ['McDonald's - Big Mac'] becauses this will be given to pyhton this return error. # If the company name is unknown, only include the product name without the company name.""" # ans = groq_inference(prompt + f"the text is: {text}") ans = DDGS().chat(prompt+"the text is \n" + text, model='claude-3-haiku') return ast.literal_eval(ans) def AI_Company_Summary(news_summaries): ans = DDGS().chat("Analyze the following combined summaries about multiple products. Provide a detailed summary of each product individually, clearly outlining their features, market relevance, and competitive advantages, so this information can be used to analyze competitor products: " + news_summaries, model='claude-3-haiku') return ans def AI_Analysis(Product_analysis,Compitetiors_analysis): prompt = Compitetiors_analysis + "Product Information: \n " + Product_analysis system_prompt = "Analyze the following competitor and product details. Provide a thorough technical analysis of each product, focusing on its market standing, technical strengths, and areas for improvement. Offer actionable insights on how the product can be enhanced to increase sales and profitability. Compare the product with competitors, identifying gaps and opportunities for differentiation and market leadership and remember that give the analyis of the compitiors only if they are related else dont give it: " + "Competitors: \n "+ prompt, # ans = DDGS().chat(prompt, model='claude-3-haiku') # if len(prompt) > 22000: # prompt = prompt[:22000] # ans = DDGS().chat( # "Analyze the following competitor and product details. Provide a thorough technical analysis of each product, focusing on its market standing, technical strengths, and areas for improvement. Offer actionable insights on how the product can be enhanced to increase sales and profitability. Compare the product with competitors, identifying gaps and opportunities for differentiation and market leadership: " + "Competitors: \n " # + prompt, # model='claude-3-haiku') model = genai.GenerativeModel(model_name="gemini-1.5-flash") response = model.generate_content(system_prompt) return response.text def AI_Analysis(Product_analysis,Compitetiors_analysis): prompt = Compitetiors_analysis + "Product Information: \n " + Product_analysis system_prompt = "Analyze the following competitor and product details. Provide a thorough technical analysis of each product, focusing on its market standing, technical strengths, and areas for improvement. Offer actionable insights on how the product can be enhanced to increase sales and profitability. Compare the product with competitors, identifying gaps and opportunities for differentiation and market leadership and remember that give the analyis of the compitiors only if they are related else dont give it: " + "Competitors: \n "+ prompt, # ans = DDGS().chat(prompt, model='claude-3-haiku') # if len(prompt) > 22000: # prompt = prompt[:22000] # ans = DDGS().chat( # "Analyze the following competitor and product details. Provide a thorough technical analysis of each product, focusing on its market standing, technical strengths, and areas for improvement. Offer actionable insights on how the product can be enhanced to increase sales and profitability. Compare the product with competitors, identifying gaps and opportunities for differentiation and market leadership: " + "Competitors: \n " # + prompt, # model='claude-3-haiku') model = genai.GenerativeModel(model_name="gemini-1.5-flash") response = model.generate_content(system_prompt) return response.text # """ This takes input of company and return summary of the company""" def analysis_name(text): compi_urls = [i['link'] for i in text['news']][0:4] print(compi_urls) text = [jina(url) for url in compi_urls] ans = ' '.join(AI_Product_Analysis(i[:8000]) for i in text if len(i)>1500) # time.sleep(25) summ = AI_Product_Summary_prod(ans[:21000]) return summ # """" This is for knowing about the Product""" # 11 # groq def know_prod(name): ser = serper_prod(name) compi_urls = [i['link'] for i in ser['news']] text = [jina(url) for url in compi_urls] ans = ' '.join(AI_Product_Analysis(i[:8000]) for i in text if len(i)>1500) summ = AI_Product_Summary(ans[:20000],name) return summ # """ This takes input of company and return list of the compi""" # 11 def compi_main(name): ret = serper_compi(name) compi_urls = [i['link'] for i in ret['organic']] title_list= [i['title'] for i in ret['organic']] text = [jina(url) for url in compi_urls] ans = ' '.join(AI_Search_compi(i[:8000],title_list,name) for i in text if len(i)>1500) lst = AI_Search_extract_cmpy(ans) return lst # """ This takes input of company list and return summary of the company""" #21 def summary_name(otp): names=[] title=otp for idx, i in enumerate(otp): results = serper_prod(i) # Call your function with the company name globals()[f'compi_{chr(65 + idx)}'] = results # print(i) # print(f'compi_{chr(65 + idx)}') names.append(f'compi_{chr(65 + idx)}') text_data = [globals()[name] for name in names] summ_data = [analysis_name(i) for i in text_data] cmpy_summ = AI_Company_Summary(" ".join(summ_data)) return cmpy_summ # """ This takes inputs of compi summary and product summary and return summary of the company""" #1 def analysis_prod(prod_summ,cmpy_summ): return AI_Analysis(prod_summ,cmpy_summ) # def main(name,email_id): # start_time = time.time() # prod_info = know_prod(name) # end_prd_anal = time.time() # print(prod_info) # print(f"Time taken to analyze the product: {end_prd_anal - start_time} seconds") # print('*****************************************************************') # # print(prod_info) # print(len(prod_info)) # otp = compi_main(name) # end_compi = time.time() # print(len(otp)) # print(f"Time taken to analyze the competitors: {end_compi - end_prd_anal} seconds") # print('*****************************************************************') # # print(otp) # summpop = summary_name(otp) # print(len(summpop)) # end_time_summary = time.time() # print(f"Time taken to analyze the summary: {end_time_summary - end_compi} seconds") # print('*****************************************************************') # # print(summpop) # print('*****************************************************************') # analysis_total = analysis_prod(prod_info,summpop) # end_time = time.time() # print(f"Analysis time taken: {end_time - end_time_summary} seconds") # print('*****************************************************************') # print(f"Total time taken: {end_time - start_time} seconds") # print('*****************************************************************') # # print(analysis(prod_info,summpop)) # #Send emails # send_email_gmail(email_id,analysis_total) # return start_time, end_prd_anal, end_compi, end_time_summary , end_time, analysis_total def time_calculator(start_time, end_time): time_in_seconds = end_time - start_time minutes = int(time_in_seconds // 60) # Get minutes seconds = int(time_in_seconds % 60) time_taken = f"{minutes} minutes and {seconds} seconds" return time_taken def send_email_gmail(receiver_email,markdown_content): sender_email = "srishnotebooks@gmail.com" sender_password = "zoge jatp yaib qtsz"# replace with the app password generated # receiver_email = "recipient_email@example.com" subject = "Product Analysis Report" # Create the email message container msg = MIMEMultipart('alternative') msg['From'] = sender_email msg['To'] = receiver_email msg['Subject'] = subject # Convert markdown to HTML html_content = markdown.markdown(markdown_content) # Attach the HTML content msg.attach(MIMEText(html_content, 'html')) try: # Set up the server using Gmail's SMTP server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() # Encrypt the connection server.login(sender_email, sender_password) # Use App Password instead of Gmail password # Send the email server.sendmail(sender_email, receiver_email, msg.as_string()) server.quit() print("Email sent successfully!") # return True except Exception as e: print(f"Error sending email: {str(e)}") # return False # def main(name, email_id): # start_time = time.time() # # Display the process in Streamlit # st.write("Analyzing product information...") # prod_info = know_prod(name) # end_prd_anal = time.time() # # Show product info and analysis time # st.write("Product Information:") # st.write(prod_info) # st.write(f"Time taken to analyze the product: {end_prd_anal - start_time} seconds") # st.write('*****************************************************************') # st.write(f"Number of websites analyzed: {len(prod_info)}") # # Competitor analysis # st.write("Analyzing competitors...") # otp = compi_main(name) # end_compi = time.time() # st.write(f"Number of competitors found: {len(otp)}") # st.write(f"Time taken to analyze the competitors: {end_compi - end_prd_anal} seconds") # st.write('*****************************************************************') # # Summary analysis # st.write("Generating summary...") # summpop = summary_name(otp) # end_time_summary = time.time() # # st.write('## Compititors Summary') # # st.write(f"len(summpop)") # st.write(f"Time taken to generate summary: {end_time_summary - end_compi} seconds") # st.write('*****************************************************************') # # Total analysis # analysis_total = analysis_prod(prod_info, summpop) # end_time = time.time() # st.write("## Total Analysis") # st.write(analysis_total) # # st.write(f"Analysis time taken: {end_time - end_time_summary} seconds") # # st.write(f"Total time taken: {end_time - start_time} seconds") # st.write('*****************************************************************') # # Send email # send_email_gmail(email_id, analysis_total) # return start_time, end_prd_anal, end_compi, end_time_summary, end_time, analysis_total # def main(name, email_id): # start_time = time.time() # # Use container to group elements in a card-like style # with st.container(): # st.markdown('---') # Horizontal line before the section # with st.spinner("Analyzing product information..."): # prod_info = know_prod(name) # end_prd_anal = time.time() # # Show product info and analysis time # st.write("### Product Information:") # st.write(prod_info) # st.write(f"Time taken to analyze the product: {end_prd_anal - start_time:.2f} seconds") # st.write(f"Number of websites analyzed: {len(prod_info)}") # st.markdown('---') # Horizontal line after the section # with st.container(): # st.markdown('---') # Horizontal line before the section # with st.spinner("Analyzing competitors..."): # otp = compi_main(name) # end_compi = time.time() # st.write("### Competitor Analysis:") # st.write(f"Number of competitors found: {len(otp)}") # st.write(f"Time taken to analyze the competitors: {end_compi - end_prd_anal:.2f} seconds") # st.markdown('---') # Horizontal line after the section # with st.container(): # st.markdown('---') # Horizontal line before the section # with st.spinner("Generating summary..."): # summpop = summary_name(otp) # end_time_summary = time.time() # st.write("### Summary Analysis:") # st.write(f"Time taken to generate summary: {end_time_summary - end_compi:.2f} seconds") # st.markdown('---') # Horizontal line after the section # with st.container(): # st.markdown('---') # Horizontal line before the section # st.write("### Total Analysis:") # analysis_total = analysis_prod(prod_info, summpop) # st.write(analysis_total) # end_time = time.time() # st.write(f"Analysis time taken: {end_time - end_time_summary:.2f} seconds") # st.write(f"Total time taken: {end_time - start_time:.2f} seconds") # st.markdown('---') # Horizontal line after the section # # Email handling # if not email_id: # st.error("Email is not sent because it was not provided.", icon="🚫") # else: # # Call send_email_gmail and check if email is sent # if send_email_gmail(email_id, analysis_total): # st.success("Email sent successfully!", icon="✅") # else: # st.error("Failed to send email.", icon="❌") # return start_time, end_prd_anal, end_compi, end_time_summary, end_time, analysis_total # # Streamlit UI # st.title("Product and Competitor Analysis") # # Inputs from user # name = st.text_input("Enter the Product Name", "Cafe Coffee Day") # email_id = st.text_input("Enter your Email", "kapishrachamalla32@gmail.com") # if st.button("Start Analysis"): # t1, t2, t3, t4, t5, analysis = main(name, email_id) # # Time breakdown in minutes # st.write("Time taken to know about the product:", (t2 - t1) / 60, "minutes") # st.write("Time taken to know about the competitors:", (t3 - t2) / 60, "minutes") # st.write("Time taken to give analysis:", (t4 - t3) / 60, "minutes") # st.write("Time taken to generate summary:", (t5 - t4) / 60, "minutes") # st.write("Total time taken:", (t5 - t1) / 60, "minutes") #$%$%$ def colored_container(color, content): st.markdown( f"""
{content}
""", unsafe_allow_html=True ) def main(name, email_id): start_time = time.time() # colored_container("#A9DFBF", f""" #

Total Analysis:

#

## Technical Analysis of McDonald's Chicken Big Mac

#

Market Standing:
The Chicken Big Mac represents a strategic move by McDonald's to tap into the growing demand for chicken-based menu items in the fast-food industry. This trend is driven by consumer interest in healthier options, a wider variety of protein sources, and the growing popularity of chicken sandwiches in general.

#

Technical Strengths:

# #

Areas for Improvement:

# #

Actionable Insights:

# #

Comparison to Competitors:

#

McDonald's needs to analyze the competitive landscape of chicken sandwiches. This includes identifying key competitors like Chick-fil-A, Wendy's, and Popeyes, and comparing their offerings in terms of flavor profiles, ingredients, and marketing strategies. This analysis will help McDonald's identify potential gaps and opportunities for differentiation.

#

Opportunities for Market Leadership:

# #

Conclusion:

#

The Chicken Big Mac holds significant potential for McDonald's to expand its market share in the growing chicken sandwich segment. By addressing its weaknesses, leveraging its strengths, and actively monitoring competitive offerings, McDonald's can create a successful product that drives sales and profitability.

#

Analysis time taken: 4.51 seconds

#

Total time taken: 368.86 seconds

# """) # Product information section with st.container(): st.markdown('---') # Horizontal line before the section with st.spinner("Analyzing product information..."): prod_info = know_prod(name) end_prd_anal = time.time() # Display product info in a colored container # colored_container("#D6EAF8", f""" #

Product Information:

#

{prod_info}

#

Time taken to analyze the product: {end_prd_anal - start_time:.2f} seconds

#

Number of websites analyzed: {len(prod_info)}

# """) st.write("### Product Information:") st.write(prod_info) # st.write(f"Time taken to analyze the product: {end_prd_anal - start_time:.2f} seconds") st.write(f"Number of Articles analyzed: {len(prod_info)}") st.markdown('---') # Horizontal line after the section # Competitor analysis section with st.container(): st.markdown('---') # Horizontal line before the section with st.spinner("Analyzing competitors..."): otp = compi_main(name) end_compi = time.time() # Display competitor info in a different colored container # colored_container("#F9E79F", f""" #

Competitor Analysis:

#

Number of competitors found: {len(otp)}

#

Time taken to analyze the competitors: {end_compi - end_prd_anal:.2f} seconds

# """) st.write("### Competitor Analysis:") st.write(f"Number of potential competitors found: {len(otp)}") # st.write(f"The Competitors are: \n {otp}") # st.write(f"Time taken to analyze the competitors: {end_compi - end_prd_anal:.2f} seconds") # st.markdown('---') # Horizontal line after the section # Summary analysis section # with st.container(): # st.markdown('---') # Horizontal line before the section with st.spinner("Generating summary..."): summpop = summary_name(otp) end_time_summary = time.time() # Display summary in a different color container # colored_container("#A9DFBF", f""" #

Summary Analysis:

#

Time taken to generate summary: {end_time_summary - end_compi:.2f} seconds

# """) st.write("### Summary Analysis:") st.write(summpop) # st.write(f"Time taken to generate summary: {end_time_summary - end_compi:.2f} seconds") st.markdown('---') # Horizontal line after the section # Total analysis section with st.container(): st.markdown('---') # Horizontal line before the section # Display total analysis in another color container analysis_total = analysis_prod(prod_info, summpop) end_time = time.time() # colored_container("#F5B7B1", f""" #

Total Analysis:

#

{analysis_total}

#

Analysis time taken: {end_time - end_time_summary:.2f} seconds

#

Total time taken: {end_time - start_time:.2f} seconds

# """) # st.write("### Total Analysis:") st.write(analysis_total) st.markdown('---') # Horizontal line after the section # Email handling if not email_id: st.error("Email is not sent because it was not provided.", icon="🚫") else: # Call send_email_gmail and check if email is sent send_email_gmail(email_id, analysis_total) st.success("Email sent successfully!", icon="✅") # else: # st.error("Failed to send email.", icon="❌") return start_time, end_prd_anal, end_compi, end_time_summary, end_time, analysis_total # Streamlit UI st.title("Market Mind 🧠") # st.subheader("Empowering You with ") st.markdown("Real-Time Market Intelligence", unsafe_allow_html=True) # Sidebar for developer profiles and hackathon info st.sidebar.markdown( """

🚀 Hackathon Project

""", unsafe_allow_html=True ) st.sidebar.markdown("Welcome to the MarketMind project, developed for the hackathon to showcase AI power in the product and competitor analysis. 🚀") # Add some icons/emojis to make it look more engaging st.sidebar.markdown("### 🔧 Project Features") # st.sidebar.markdown("- Analyze product details using OpenFoodFacts API.") st.sidebar.markdown("- Real-Time Market Intelligence: Offers real-time data updates for informed decision-making") st.sidebar.markdown("- AI and Machine Learning: Helps analyze competitors and suggests improvement strategies based on data.") # Developer details with LinkedIn links st.sidebar.markdown("### 👨‍💻 Developers") st.sidebar.markdown("[Srish](https://www.linkedin.com/in/srishrachamalla/) - AI/ML Developer") st.sidebar.markdown("[Sai Teja](https://www.linkedin.com/in/saiteja-pallerla-668734225/) - Data Analyst") # Add expander sections for additional content with st.sidebar.expander("ℹ About MarketMind"): st.write("MarketMind is a platform focused on providing advanced data analytics, market intelligence, and AI-driven insights for businesses, investors, and market professionals. Its solutions are aimed at helping organizations make informed decisions by analyzing vast amounts of market data, consumer behavior, and industry trends in real-time") with st.sidebar.expander("📚 Useful Resources"): st.write("[Google Gemini AI Documentation](https://ai.google.dev/gemini-api/docs)") st.write("[Streamlit Documentation](https://docs.streamlit.io/)") st.write("[Groq Documentation](https://console.groq.com/docs/quickstart)") # Add progress indicator for hackathon phases or development stages st.sidebar.markdown("### ⏳ Hackathon Progress") st.sidebar.progress(0.99) # Set progress level (0 to 1) # Sidebar footer with final notes st.sidebar.markdown("---") st.sidebar.markdown( """
Developed by Srish & Sai Teja • Powered by Google Gemini AI
""", unsafe_allow_html=True ) # Inputs from user name = st.text_input("Enter the Product Name", "Cafe Coffee Day") email_id = st.text_input("Enter your Email", "") if st.button("Start Analysis"): t1, t2, t3, t4, t5, analysis = main(name, email_id) # # Time breakdown in minutes # st.write("### Time Breakdown (in minutes)") # st.write(f"Time taken to analyze the product: {(t2 - t1) / 60:.2f} minutes") # st.write(f"Time taken to analyze competitors: {(t3 - t2) / 60:.2f} minutes") # st.write(f"Time taken to generate summary: {(t4 - t3) / 60:.2f} minutes") # st.write(f"Time taken for total analysis: {(t5 - t1) / 60:.2f} minutes") # Time breakdown in minutes st.write("### Time Breakdown (in minutes)") # Define colors for each breakdown total_color = "#FF5733" # Red competitors_color = "#33C1FF" # Blue summary_color = "#75FF33" # Green product_color = "#FF33B5" # Pink # Display each time taken in different colors # Get seconds st.markdown(f"

Time taken to analyze the product: {time_calculator(t2 , t1)}

", unsafe_allow_html=True) st.markdown(f"

Time taken to analyze competitors: {time_calculator(t3 , t2)} minutes

", unsafe_allow_html=True) st.markdown(f"

Time taken to generate summary: {time_calculator(t4 , t3)} minutes

", unsafe_allow_html=True) st.markdown(f"

Time taken for total analysis: {time_calculator(t5 , t1)} minutes

", unsafe_allow_html=True) st.markdown("---") st.markdown("""

MarketMind was developed for a hackathon using Streamlit to showcase AI power in product and competitor analysis.

Developed by Srish & Sai Teja

""", unsafe_allow_html=True) # if __name__ == "__main__": # t1,t2,t3,t4,t5,analysis = main('Cafe Coffee Day',"kapishrachamalla32@gmail.com") # print("time taken to Know about the product: ", (t2-t1)/60) # print("time taken to Know about the competitors: ", (t3-t2)/60) # print("time taken to Know about the give analysis: ", (t4-t3)/60) # print("time taken to Know about the summary: ", (t5-t4)/60) # print("total time taken: ", (t5-t1)/60) # import ast # import requests # import json # import time # import streamlit as st # from duckduckgo_search import DDGS # import google.generativeai as genai # from groq import Groq # import smtplib # from email.mime.text import MIMEText # from email.mime.multipart import MIMEMultipart # import markdown # # Configure generative AI API key # genai.configure(api_key='AIzaSyCootL_jwKI3YDb6cKRJV-Ad0N4oKlLXXE') # client = Groq(api_key='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') # # Function definitions as in your original code # def jina(url): # base_url= "https://r.jina.ai//" # url=base_url+url # response=requests.get(url) # return response.text # def groq_inference(query): # completion = client.chat.completions.create( # model="llama3-groq-70b-8192-tool-use-preview", # messages=[{"role": "user", "content": query}], # temperature=0, # max_tokens=2040, # top_p=0.65, # stop=None, # ) # return completion.choices[0].message.content # def serper_prod(company): # url = "https://google.serper.dev/news" # payload = json.dumps({"q": f"{company} info"}) # headers = { # 'X-API-KEY': '7d6a39f71072f99cd421dbdd6cfebc73e2a66a07', # 'Content-Type': 'application/json' # } # response = requests.request("POST", url, headers=headers, data=payload) # return json.loads(response.text) # def serper_compi(company): # url = "https://google.serper.dev/search" # payload = json.dumps({"q": f"{company} competitors."}) # headers = { # 'X-API-KEY': '7d6a39f71072f99cd421dbdd6cfebc73e2a66a07', # 'Content-Type': 'application/json' # } # response = requests.request("POST", url, headers=headers, data=payload) # return json.loads(response.text) # def AI_Search_compi(text, titles, name): # ans = groq_inference(f"""Summarize the text of the articles titles are {titles} and don't remove the important terms about products or applications which should help in knowing about the competitors for a company {name}. The data is: {text}""") # return ans # def AI_Product_Analysis(text): # ans = DDGS().chat("Analyze the products mentioned in the following news article in 400 words or fewer. Focus on their features, market relevance, and potential impact for a company's market planning: " + text, model='claude-3-haiku') # return ans # def AI_Product_Summary(news_summaries, product): # ans = groq_inference(f"""Create a comprehensive summary of the product {product} based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost, and you may add text from the articles as well: {news_summaries}""") # return ans # def AI_Search_extract_cmpy(text): # prompt = """You will be given a dynamic text that summarizes key products, applications, and comparisons from articles. Your task is to extract relevant product names from the text and generate a list of search queries suitable for a search API. Don't give more than five names in the list. Output Format: ['company - product name', 'company - product name', 'product name', ...].""" # ans = DDGS().chat(prompt + "The text is \n" + text, model='claude-3-haiku') # print(ans) # return ast.literal_eval(ans) # def AI_Company_Summary(news_summaries): # ans = DDGS().chat("Analyze the following combined summaries about multiple products. Provide a detailed summary of each product individually, clearly outlining their features, market relevance, and competitive advantages: " + news_summaries, model='claude-3-haiku') # return ans # def AI_Product_Summary_prod(news_summaries): # # combined_summaries = " ".join(news_summaries) # ans = groq_inference(f"""Create a comprehensive summary of the product based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost and remember that these are form the news articles so you may have add text also : """ + news_summaries) # # ans = DDGS().chat("Create a comprehensive summary of the product based on the following summaries of 10 or fewer news articles. Ensure no important product details are lost: " + news_summaries, model='gpt-4o-mini') # def AI_Analysis(Product_analysis, Compitetiors_analysis): # prompt = Compitetiors_analysis + "Product Information: \n " + Product_analysis # system_prompt = f"""Analyze the following competitor and product details. Provide a technical analysis of each product, focusing on its market standing, technical strengths, and areas for improvement. Compare the product with competitors, identifying gaps and opportunities for differentiation: Competitors: {prompt}""" # model = genai.GenerativeModel(model_name="gemini-1.5-flash") # response = model.generate_content(system_prompt) # return response.text # def send_email_gmail(receiver_email,markdown_content): # sender_email = "srishnotebooks@gmail.com" # sender_password = "zoge jatp yaib qtsz"# replace with the app password generated # # receiver_email = "recipient_email@example.com" # subject = "Product Analysis Report" # # Create the email message container # msg = MIMEMultipart('alternative') # msg['From'] = sender_email # msg['To'] = receiver_email # msg['Subject'] = subject # # Convert markdown to HTML # html_content = markdown.markdown(markdown_content) # # Attach the HTML content # msg.attach(MIMEText(html_content, 'html')) # try: # # Set up the server using Gmail's SMTP # server = smtplib.SMTP('smtp.gmail.com', 587) # server.starttls() # Encrypt the connection # server.login(sender_email, sender_password) # Use App Password instead of Gmail password # # Send the email # server.sendmail(sender_email, receiver_email, msg.as_string()) # server.quit() # print("Email sent successfully!") # except Exception as e: # print(f"Error sending email: {str(e)}") # # """ This takes input of company and return summary of the company""" # def analysis_name(text): # compi_urls = [i['link'] for i in text['news']][0:4] # print(compi_urls) # text = [jina(url) for url in compi_urls] # ans = ' '.join(AI_Product_Analysis(i[:8000]) for i in text if len(i)>1500) # # time.sleep(25) # summ = AI_Product_Summary_prod(ans[:21000]) # return summ # # """" This is for knowing about the Product""" # # 11 # # groq # def know_prod(name): # ser = serper_prod(name) # compi_urls = [i['link'] for i in ser['news']] # text = [jina(url) for url in compi_urls] # ans = ' '.join(AI_Product_Analysis(i[:8000]) for i in text if len(i)>1500) # summ = AI_Product_Summary(ans[:20000],name) # return summ # # """ This takes input of company and return list of the compi""" # # 11 # def compi_main(name): # ret = serper_compi(name) # compi_urls = [i['link'] for i in ret['organic']] # title_list= [i['title'] for i in ret['organic']] # text = [jina(url) for url in compi_urls] # ans = ' '.join(AI_Search_compi(i[:8000],title_list,name) for i in text if len(i)>1500) # lst = AI_Search_extract_cmpy(ans) # return lst # # """ This takes input of company list and return summary of the company""" # #21 # def summary_name(otp): # names=[] # title=otp # for idx, i in enumerate(otp): # results = serper_prod(i) # Call your function with the company name # globals()[f'compi_{chr(65 + idx)}'] = results # # print(i) # # print(f'compi_{chr(65 + idx)}') # names.append(f'compi_{chr(65 + idx)}') # text_data = [globals()[name] for name in names] # summ_data = [analysis_name(i) for i in text_data] # cmpy_summ = AI_Company_Summary(" ".join(summ_data)) # return cmpy_summ # # """ This takes inputs of compi summary and product summary and return summary of the company""" # #1 # def analysis_prod(prod_summ,cmpy_summ): # return AI_Analysis(prod_summ,cmpy_summ) # # Streamlit Interface # def main(): # st.title("Product and Competitor Analysis Tool") # # Input company name # company_name = st.text_input("Enter Company Name", value="Sample Company") # email = st.text_input("Enter your email (Optional)") # # Perform Analysis # if st.button("Analyze"): # start_time = time.time() # st.write(f"Analyzing {company_name}...") # # Analyze product # prod_info = know_prod(company_name) # st.write("### Product Information:") # st.write(prod_info) # end_prd_anal = time.time() # st.write(f"Time taken to analyze the product: {(end_prd_anal - start_time)/60} Mins") # # Analyze competitors # st.write("### Competitor Information:") # otp = compi_main(company_name) # print('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$') # print(otp) # st.write(otp) # end_compi = time.time() # st.write(f"Time taken to analyze the competitors: {end_compi - end_prd_anal} seconds") # # Summary # st.write("### Summary:") # cmpy_summ = summary_name(otp) # st.write(cmpy_summ) # end_time_summary = time.time() # st.write(f"Time taken to generate summary: {end_time_summary - end_compi} seconds") # analysis_total = analysis_prod(prod_info,cmpy_summ) # st.write("### Analysis:" + analysis_total) # # Send via email (Optional) # if email: # send_email_gmail(email, analysis_total) # st.write(f"Sending analysis to {email}...") # # (Implement email sending logic here) # # ... # if __name__ == "__main__": # main()