import requests import json import wikipedia import requests from bs4 import BeautifulSoup import gradio as gr from transformers import pipeline # Set up Google SERP API credentials serp_api_key = '5924c6cfe5fec240e39838ff06439c8d36d294a0' # Replace with your actual Google SERP API key # Function to send a message and receive a response from the chatbot def chat(message): try: # You can add your chatbot implementation here return "This is a dummy chat response." except Exception as e: print("An error occurred:", e) return "" # Function to get the latest answers from Google SERP API def get_latest_answers(query): url = "https://google.serper.dev/search" payload = json.dumps({ "q": query }) headers = { 'X-API-KEY': serp_api_key, 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) try: # Parse the response JSON data = json.loads(response.text) # Extract details from the response output = "" if 'knowledgeGraph' in data: knowledge_graph = data['knowledgeGraph'] output += "Website: {}\n".format(knowledge_graph.get('website')) output += "Description: {}\n".format(knowledge_graph.get('description')) if 'organic' in data: organic_results = data['organic'] for result in organic_results: output += "Snippet: {}\n".format(result.get('snippet')) if 'peopleAlsoAsk' in data: people_also_ask = data['peopleAlsoAsk'] for question in people_also_ask: output += "Snippet: {}\n".format(question.get('snippet')) return output except json.JSONDecodeError: print(".") return "" except Exception as e: print(".") return "" # Function to search Wikipedia for an answer and summarize it def search_wikipedia(query): try: search_results = wikipedia.search(query) # Get the page summary of the first search result if search_results: page_title = search_results[0] page_summary = wikipedia.summary(page_title) return page_summary else: print(".") return None except wikipedia.exceptions.DisambiguationError as e: # Handle disambiguation error print(".") return None except wikipedia.exceptions.PageError as e: # Handle page not found error print(".") return None except Exception as e: # Handle other exceptions print(".") return None # Function to generate summarized paragraph using transformer-based summarization def generate_summary(user_input): output = get_latest_answers(user_input) page_summary = search_wikipedia(user_input) chat_answer = chat(user_input) # Generate summarized paragraph using transformer-based summarization summarizer = pipeline("summarization") input_text = f"{output}\n {page_summary}\n" summarized_paragraph = summarizer(input_text, max_length=200, do_sample=True)[0]['summary_text'] return summarized_paragraph # Define the Gradio interface def summarizer_interface(user_input): summarized_text = generate_summary(user_input) return summarized_text iface = gr.Interface( fn=summarizer_interface, inputs="text", outputs="text", title="Osana Web-GPT", description="Enter your query and get the latest and better answer.", theme="black", layout="horizontal", ) # Launch the interface iface.launch()