Create app.py

app.py

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+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"\n{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()