235 lines
8.6 KiB
Python
235 lines
8.6 KiB
Python
"""
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Gradio interface for the intelligent research system.
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This module provides a web interface for users to interact with the research system.
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"""
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import os
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import json
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import gradio as gr
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import sys
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import time
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from pathlib import Path
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# Add the parent directory to the path to allow importing from other modules
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sys.path.append(str(Path(__file__).parent.parent))
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from query.query_processor import QueryProcessor
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from execution.search_executor import SearchExecutor
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from execution.result_collector import ResultCollector
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class GradioInterface:
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"""Gradio interface for the intelligent research system."""
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def __init__(self):
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"""Initialize the Gradio interface."""
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self.query_processor = QueryProcessor()
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self.search_executor = SearchExecutor()
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self.result_collector = ResultCollector()
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self.results_dir = Path(__file__).parent.parent / "results"
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self.results_dir.mkdir(exist_ok=True)
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def process_query(self, query, num_results=10):
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"""
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Process a query and return the results.
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Args:
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query (str): The query to process
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num_results (int): Number of results to return
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Returns:
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tuple: (markdown_results, json_results_path)
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"""
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try:
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# Process the query
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print(f"Processing query: {query}")
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processed_query = self.query_processor.process_query(query)
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print(f"Processed query: {processed_query}")
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# Get available search engines and print their status
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available_engines = self.search_executor.get_available_search_engines()
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print(f"Available search engines: {available_engines}")
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# Check which handlers are actually available
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for engine_name, handler in self.search_executor.available_handlers.items():
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print(f"Handler {engine_name} available: {handler.is_available()}")
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if not handler.is_available():
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print(f" - Reason: API key may be missing for {engine_name}")
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# Add search engines if not specified
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if 'search_engines' not in processed_query:
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processed_query['search_engines'] = available_engines
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print(f"Using search engines: {available_engines}")
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# Execute the search - request more results from each engine
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print(f"Executing search...")
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search_results = self.search_executor.execute_search(
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structured_query=processed_query,
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num_results=num_results
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)
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# Print which engines returned results
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for engine, results in search_results.items():
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print(f"Engine {engine} returned {len(results)} results")
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# Process the results - don't limit the number of results
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print(f"Processing results...")
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processed_results = self.result_collector.process_results(
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search_results, dedup=True, max_results=None
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)
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print(f"Processed {len(processed_results)} results")
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# Save results to file
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timestamp = int(time.time())
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results_file = self.results_dir / f"results_{timestamp}.json"
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# Ensure the results are not empty before saving
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if processed_results:
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with open(results_file, "w") as f:
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json.dump(processed_results, f, indent=2)
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print(f"Results saved to {results_file}")
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file_path = str(results_file)
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else:
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error_message = "No results found. Please try a different query or check API keys."
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print(error_message)
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file_path = None
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return f"## No Results Found\n\n{error_message}", file_path
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# Format results for display
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markdown_results = self._format_results_as_markdown(processed_results)
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return markdown_results, file_path
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except Exception as e:
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error_message = f"Error processing query: {str(e)}"
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print(f"ERROR: {error_message}")
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import traceback
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traceback.print_exc()
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return f"## Error\n\n{error_message}", None
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def _format_results_as_markdown(self, results):
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"""
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Format results as markdown.
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Args:
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results (list): List of result dictionaries
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Returns:
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str: Markdown formatted results
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"""
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if not results:
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return "## No Results Found\n\nNo results were found for your query."
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# Count results by source
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source_counts = {}
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for result in results:
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source = result.get("source", "unknown")
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source_counts[source] = source_counts.get(source, 0) + 1
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# Create source distribution string
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source_distribution = ", ".join([f"{source}: {count}" for source, count in source_counts.items()])
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markdown = f"## Search Results\n\n"
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markdown += f"*Sources: {source_distribution}*\n\n"
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for i, result in enumerate(results):
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title = result.get("title", "Untitled")
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url = result.get("url", "")
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snippet = result.get("snippet", "No snippet available")
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source = result.get("source", "unknown")
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authors = result.get("authors", "Unknown")
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year = result.get("year", "Unknown")
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score = result.get("relevance_score", 0)
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markdown += f"### {i+1}. {title}\n\n"
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markdown += f"**Source**: {source}\n\n"
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markdown += f"**URL**: [{url}]({url})\n\n"
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markdown += f"**Snippet**: {snippet}\n\n"
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markdown += f"**Authors**: {authors}\n\n"
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markdown += f"**Year**: {year}\n\n"
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markdown += f"**Score**: {score}\n\n"
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markdown += "---\n\n"
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return markdown
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def create_interface(self):
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"""
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Create and return the Gradio interface.
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Returns:
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gr.Blocks: The Gradio interface
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"""
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with gr.Blocks(title="Intelligent Research System") as interface:
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gr.Markdown("# Intelligent Research System")
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gr.Markdown(
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"""
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This system helps you research topics by searching across multiple sources
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including Google (via Serper), Google Scholar, and arXiv.
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"""
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)
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with gr.Row():
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with gr.Column(scale=4):
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query_input = gr.Textbox(
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label="Research Query",
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placeholder="Enter your research question here...",
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lines=3
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)
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with gr.Column(scale=1):
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num_results = gr.Slider(
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minimum=5,
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maximum=50,
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value=20,
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step=5,
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label="Results Per Engine"
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)
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search_button = gr.Button("Search", variant="primary")
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gr.Examples(
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examples=[
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["What are the latest advancements in quantum computing?"],
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["Compare transformer and RNN architectures for NLP tasks"],
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["Explain the environmental impact of electric vehicles"]
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],
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inputs=query_input
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)
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with gr.Row():
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with gr.Column():
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results_output = gr.Markdown(label="Results")
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with gr.Row():
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with gr.Column():
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file_output = gr.Textbox(
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label="Results saved to file",
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interactive=False
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)
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search_button.click(
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fn=self.process_query,
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inputs=[query_input, num_results],
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outputs=[results_output, file_output]
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)
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return interface
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def launch(self, **kwargs):
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"""
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Launch the Gradio interface.
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Args:
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**kwargs: Keyword arguments to pass to gr.Interface.launch()
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"""
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interface = self.create_interface()
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interface.launch(**kwargs)
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def main():
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"""Main function to launch the Gradio interface."""
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interface = GradioInterface()
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interface.launch(share=True)
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if __name__ == "__main__":
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main()
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