import gradio as gr
from openai import OpenAI
import os
import json
from functools import partial
# Initialize OpenAI client with API key and base URL from environment variables
client = OpenAI(
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_BASE_URL"]
)
# Define the number of results per page and total results to generate
RESULTS_PER_PAGE = 10
TOTAL_RESULTS = 30 # Generate 30 results to allow pagination
def fetch_search_results(query):
"""Fetch search results from the LLM based on the user's query."""
if not query.strip():
return None, "Please enter a search query."
prompt = f"""
You are a search engine that provides informative and relevant results. For the given query '{query}',
generate {TOTAL_RESULTS} search results. Each result should include:
- 'title': A concise, descriptive title of the result.
- 'snippet': A short summary (2-3 sentences) of the content.
- 'url': A plausible, clickable URL where the information might be found (e.g., a real or hypothetical website).
Format the response as a JSON array of objects, where each object has 'title', 'snippet', and 'url' fields.
Ensure the results are diverse, relevant to the query, and the URLs are realistic (e.g., https://example.com/page).
"""
try:
response = client.chat.completions.create(
model="gemini-2.0-flash-lite", # Updated model name
messages=[
{"role": "system", "content": "You are a helpful search engine."},
{"role": "user", "content": prompt}
],
response_format={"type": "json_object"}
)
content = response.choices[0].message.content
results = json.loads(content)
# Handle different possible JSON structures
if isinstance(results, dict) and "results" in results:
results = results["results"]
elif isinstance(results, list):
pass
else:
return None, "Error: Unexpected JSON structure."
return results, None
except Exception as e:
error_msg = str(e)
if "404" in error_msg:
return None, f"Error 404: Model or endpoint not found. Check OPENAI_BASE_URL ({os.environ['OPENAI_BASE_URL']}) and model name."
elif "401" in error_msg:
return None, "Error 401: Invalid API key. Check OPENAI_API_KEY."
else:
return None, f"Error: {error_msg}"
def generate_search_page(query, page=1):
"""Generate a full HTML search results page."""
results, error = fetch_search_results(query)
if error:
return f"""
LLM Search Engine
LLM Search Engine
{error}
"""
# Calculate pagination boundaries
start_idx = (page - 1) * RESULTS_PER_PAGE
end_idx = start_idx + RESULTS_PER_PAGE
total_pages = (len(results) + RESULTS_PER_PAGE - 1) // RESULTS_PER_PAGE
# Ensure indices are within bounds
if start_idx >= len(results):
return """
LLM Search Engine
LLM Search Engine
No more results to display.
"""
paginated_results = results[start_idx:end_idx]
# Generate full HTML page
html = """
LLM Search Engine
LLM Search Engine
Results for '{query}' (Page {page} of {total_pages})
"""
# Add search results
for result in paginated_results:
title = result.get("title", "No title")
snippet = result.get("snippet", "No snippet")
url = result.get("url", "#")
html += f"""
"""
# Add pagination
html += ''
html += """
"""
return html
# Define the app with Blocks
with gr.Blocks(title="LLM Search Engine") as app:
# Custom route handler
def handle_request(query, page):
try:
page = int(page) if page else 1
except (ValueError, TypeError):
page = 1
return generate_search_page(query, page)
# Use a Route to serve raw HTML
app.route("/", inputs=[gr.Textbox(visible=False, value=""), gr.Number(visible=False, value=1)],
outputs=gr.HTML(), _js="() => [new URLSearchParams(window.location.search).get('query') || '', new URLSearchParams(window.location.search).get('page') || '1']",
fn=handle_request)
app.launch()