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app.py

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+import openai
+from googleapiclient.discovery import build
+import requests
+import json
+import wikipedia
+import requests
+from bs4 import BeautifulSoup
+import gradio as gr
+
+# Set up the OpenAI API client
+openai.api_key = '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'  # Replace with your actual API key
+
+# Set up your YouTube Data API credentials
+youtube_api_key = 'AIzaSyDYzXAkPqU6ODnGX9rEEcvL64xh29_LRVs'  # Replace with your actual YouTube API key
+
+# Set up Google SERP API credentials
+serp_api_key = '03c74289238ba82d2889379e7a958a07b56c45de'  # Replace with your actual Google SERP API key
+
+# Function to send a message and receive a response from the chatbot
+def chat(message):
+    try:
+        response = openai.Completion.create(
+            engine='text-davinci-003',  # Choose the language model/engine you want to use
+            prompt=message,
+            max_tokens=50,  # Adjust the response length as needed
+            n=1,  # Number of responses to generate
+            stop=None,  # Specify a stop token to end the response
+        )
+        return response.choices[0].text.strip()
+    except Exception as e:
+        print("An error occurred:", e)
+        return ""
+
+# Function to search for YouTube videos
+def search_videos(query, max_results=5):
+    # Build the YouTube API client
+    youtube = build('youtube', 'v3', developerKey=youtube_api_key)
+
+    # Make a search request to retrieve video results
+    search_response = youtube.search().list(
+        q=query,
+        part='id',
+        maxResults=max_results,
+        type='video'
+    ).execute()
+
+    # Extract the video links from the search response
+    video_links = []
+    for item in search_response['items']:
+        video_id = item['id']['videoId']
+        video_link = f'https://www.youtube.com/watch?v={video_id}'
+        video_links.append(video_link)
+
+    return video_links
+
+# 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 OpenAI API
+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 OpenAI API
+    response = openai.Completion.create(
+        engine='text-davinci-003',
+        prompt=f"Data from Google SERP API:\n{output}\nWikipedia summary:\n{page_summary}\n\nOpenAI chat response:\n{chat_answer}\n\nSummarize the above data into a paragraph.",
+        max_tokens=200
+    )
+    summarized_paragraph = response.choices[0].text.strip()
+
+    return summarized_paragraph
+
+# Define the Gradio interface
+def summarizer_interface(user_input):
+    summarized_text = generate_summary(user_input)
+    video_links = search_videos(user_input)
+    return summarized_text, video_links
+
+iface = gr.Interface(
+    fn=summarizer_interface,
+    inputs="text",
+    outputs=["text", "text"],
+    title="Osana Web-GPT",
+    description="Enter your query and get latest and better answer.",
+    layout="horizontal",
+    examples=[
+        ["What is the capital of France?"],
+        ["How does photosynthesis work?"],
+        ["Who is the president of the United States?"],
+        ["What is the capital of Japan?"],
+        ["How do I bake a chocolate cake?"],
+        ["What is the meaning of life?"],
+        ["Who painted the Mona Lisa?"],
+        ["What is the population of New York City?"],
+        ["How does the internet work?"],
+        ["What is the largest planet in our solar system?"],
+        ["What are the benefits of regular exercise?"],
+        ]
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+beautifulsoup4==4.12.2
+google_api_python_client==2.88.0
+gradio==3.34.0
+openai==0.27.5
+Requests==2.31.0
+wikipedia==1.4.0