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import streamlit as st
import pandas as pd
import re
from openai import OpenAI
import concurrent.futures
import json
import os
def extract_and_parse_json_from_markdown(markdown_text: str) -> dict:
code_block_pattern = r"```(?:json)?\s*([\s\S]*?)```"
code_block_match = re.search(code_block_pattern, markdown_text)
if code_block_match:
json_str = code_block_match.group(1).strip()
else:
json_str = markdown_text.strip()
try:
return json.loads(json_str)
except json.JSONDecodeError as e:
raise ValueError(f"Invalid JSON format: {e}")
def process_event(event):
openai = OpenAI(
api_key=os.environ.get('DEEP_API_KEY'),
base_url="https://api.deepinfra.com/v1/openai",
)
llm_prompt = f"""
You are a digital marketing campaign analyst designed to analyze and report digital marketing campaign data for Rod Wave concerts, Your job is to convert the given text into JSON
{{
"market": "str",
"total_spend": "float",
"impressions": "float",
"clicks": "float",
"metrics_cpc": "float",
"metrics_cpm": "float",
"metrics_ctr": "float",
"metrics_cpa": "float",
"platform_spend_meta_total": "float",
"platform_spend_meta_instagram": "float",
"platform_spend_meta_facebook": "float",
"platform_spend_google_total": "float",
"platform_spend_google_youtube": "float",
"platform_spend_google_search_display": "float",
"platform_spend_programmatic": "float",
"revenue_average_ticket_price": "float",
"revenue_total_revenue": "float",
"revenue_roi": "float"
}}
Here is Text for it:
{event}
Return in only JSON adhering to Above Schema
"""
chat_completion = openai.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": llm_prompt}],
)
return chat_completion.choices[0].message.content
def process_all_events(events):
json_all = []
progress_bar = st.progress(0)
with concurrent.futures.ThreadPoolExecutor(max_workers=6) as executor:
futures = [executor.submit(process_event, event) for event in events]
for i, future in enumerate(concurrent.futures.as_completed(futures)):
progress = (i + 1) / len(events)
progress_bar.progress(progress)
# st.write(f"Processed event {i + 1}/{len(events)}")
json_all.append(future.result())
return json_all
def main():
st.title("Rod Wave Concert Marketing Data Processor")
input_method = st.radio("Choose input method:", ["Text Area", "File Upload"])
text = None
if input_method == "Text Area":
text = st.text_area("Enter concert marketing data:", height=300)
else:
uploaded_file = st.file_uploader("Choose a text file", type="txt")
if uploaded_file is not None:
text = uploaded_file.read().decode("utf-8")
if text:
events = re.split(r'\n(?=Rod Wave Concert)', text)
events = [event for event in events if event.strip()]
st.write(f"Found {len(events)} events to process")
if st.button("Process Data"):
with st.spinner("Processing events..."):
json_all = process_all_events(events)
json_sanity = []
for ele in json_all:
json_sanity.append(extract_and_parse_json_from_markdown(ele))
df = pd.DataFrame(json_sanity)
st.success("Processing complete!")
st.write("Preview of processed data:")
st.dataframe(df.head())
csv = df.to_csv(index=False)
st.download_button(
label="Download CSV",
data=csv,
file_name="processed_concert_data.csv",
mime="text/csv"
)
if __name__ == "__main__":
main() |