Spaces:
Sleeping
Sleeping
| 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() | |