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import gradio as gr
from generate_narratives import Narrative_Generator
from sentence_transformers import SentenceTransformer
import pandas as pd
from mlx_lm import load
from sim_scores import Results
from flask import Flask, jsonify
# Global variables to store results
formatted_narratives = None
clustered_tweets = None
dataset_options = {
"Trump Tweets": "trumptweets1205-127.csv",
"Dataset 2": "path/to/dataset2.csv",
"Upload Your Own": None
}
narrative_options = {
"0": "Russia is an ally",
"1": "The 2020 election was stolen"
}
summary_model, tokenizer = load("mlx-community/Mistral-Nemo-Instruct-2407-4bit")
embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
def run_narrative_generation(selected_dataset, uploaded_file, num_narratives, progress=gr.Progress()):
progress(0, desc="Generating narratives...")
file_path = dataset_options.get(selected_dataset, None)
if selected_dataset == "Upload Your Own":
if uploaded_file is None:
return "Please upload a file.", None
file_path = uploaded_file.name
generator = Narrative_Generator(summary_model, tokenizer, embedding_model, file_path, num_narratives)
try:
json_narratives, _, clustered_tweets = generator.generate_narratives(progress=progress)
formatted_narratives = generator.get_html_formatted_outputs(json_narratives)
html_output = "<div id='narratives-container' style='display: flex; flex-direction: column; gap: 10px;'>"
for narrative_html in formatted_narratives:
html_output += narrative_html
html_output += "</div>"
# Store the results globally
formatted_narratives = json_narratives
clustered_tweets = clustered_tweets
return html_output, clustered_tweets
# Show error in UI
except ValueError as e:
return str(e), None
def trace_narrative(selected_dataset, narrative, max_tweets):
"""
Runs the ranking function and returns the top k similar tweets to the selected narrative.
"""
# Make sure the file is handled correctly
# file_path = "trumptweets1205-127.csv"
file_path = dataset_options.get(selected_dataset, None)
# Load the Results object and rank the narrative
results = Results(embedding_model, file_path, max_tweets, [narrative])
top_k_results = results.print_top_k(k=10, narrative_ind=0)
# Format the result as a readable string or table
formatted_output = f"**Top 10 Most Similar Tweets to Narrative**: `{narrative}`\n\n"
if isinstance(top_k_results, pd.DataFrame):
formatted_output += top_k_results.to_markdown(index=False)
return formatted_output
# Gradio UI Setup
with gr.Blocks() as iface:
with gr.Tab("Narrative Generator"):
narrative_generator_interface = gr.Interface(
fn=run_narrative_generation,
inputs=[
gr.Dropdown(list(dataset_options.keys()), value="Trump Tweets", label="Select Dataset"),
gr.File(label="Upload Text File (Optional)"),
gr.Slider(1, 10, step=1, value=5, label="Number of Narratives"),
],
outputs=[
gr.Markdown(label="Generated Narratives"),
],
title="Trump Narrative Generator",
description="Choose a dataset or upload a file. Select the number of narratives, then click 'Run'.",
theme="huggingface"
)
with gr.Tab("Trace Narrative"):
trace_narrative_interface = gr.Interface(
fn=trace_narrative,
inputs=[
# gr.Dropdown([["Russia is an ally", "The 2020 election was stolen"]], value="Narrative", label="Select Narrative"),
gr.Dropdown(list(dataset_options.keys()), value="Trump Tweets", label="Select Dataset"),
gr.Textbox(label="Narrative", value="e.g. Russia is an ally"),
gr.Slider(1, 1000, step=1, value=10, label="Max Tweets to Process"),
],
outputs=[
gr.HTML(label="Top 10 Similar Tweets"),
gr.JSON(label="Clustered Tweets")
],
title="Rank Narrative with Tweets",
description="Select a narrative and trace the most similar tweets to that narrative.",
theme="huggingface"
)
def get_narratives():
if formatted_narratives is not None and clustered_tweets is not None:
return jsonify({
"generated_narratives": formatted_narratives, # This is the narratives output from the function
"clustered_tweets": clustered_tweets # This is the corresponding tweets list
})
else:
return jsonify({"error": "No narratives generated yet"}), 400
gr.HTML("""
<link rel="stylesheet" type="text/css" href="assets/style.css">
<script src="assets/script.js"></script>
""")
# iface.launch(share=False, inbrowser=True, server_name="localhost", server_port=7860, prevent_thread_lock=True)
iface.launch()
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