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import streamlit as st
import time
import re
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
# 1. PAGE CONFIGURATION
st.set_page_config(page_title="WhatsApp Chat Analyzer", page_icon="📱", layout="wide")
# 2. LOAD MODEL FROM HUGGING FACE
# We use @st.cache_resource so it only downloads once
@st.cache_resource
def load_pipeline():
model_id = "AishaniS/text_summarizer" # Your specific HF repository
try:
# Load directly from the Hub
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
return pipeline("summarization", model=model, tokenizer=tokenizer)
except Exception as e:
st.error(f"Error loading model from Hugging Face: {e}")
return None
summarizer = load_pipeline()
# 3. PREPROCESSING FUNCTION (Corrected for your Date/Time format)
def clean_whatsapp_log(text):
"""
Parses WhatsApp chat.
Target format: "24/12/25, 09:38 - Name: Message"
"""
# Regex Breakdown:
# \d{1,2}/\d{1,2}/\d{2,4} -> Date (e.g., 24/12/25)
# ,\s -> Comma and space
# \d{1,2}:\d{2} -> Time (e.g., 09:38 or 20:43)
# \s-\s -> " - " separator
pattern = r'\d{1,2}/\d{1,2}/\d{2,4},\s\d{1,2}:\d{2}\s-\s'
clean_lines = []
lines = text.split('\n')
for line in lines:
# Filter system messages
if "<Media omitted>" in line or "Messages and calls are end-to-end encrypted" in line:
continue
# Remove timestamp
cleaned_line = re.sub(pattern, '', line).strip()
# Only add if text remains
if cleaned_line:
clean_lines.append(cleaned_line)
return "\n".join(clean_lines)
# 4. CHUNKING FUNCTION (To handle long chats)
def chunk_text(text, max_chars=2000):
chunks = []
current_chunk = ""
for line in text.split('\n'):
if len(current_chunk) + len(line) < max_chars:
current_chunk += line + "\n"
else:
chunks.append(current_chunk)
current_chunk = line + "\n"
if current_chunk:
chunks.append(current_chunk)
return chunks
# 5. MAIN UI
st.title("📱 Real-Time WhatsApp Summarizer")
st.markdown(f"**Model:** `AishaniS/text_summarizer` | **Status:** {'✅ Loaded' if summarizer else '❌ Error'}")
st.markdown("Upload your exported `_chat.txt` file to analyze conversation.")
uploaded_file = st.file_uploader("Choose a file", type=['txt'])
if uploaded_file and summarizer:
raw_text = uploaded_file.getvalue().decode("utf-8")
# Preprocess
clean_text = clean_whatsapp_log(raw_text)
# Layout: Two columns
col1, col2 = st.columns(2)
with col1:
st.subheader("📜 Processed Chat")
st.text_area("Cleaned Input", clean_text, height=400)
with col2:
st.subheader("🤖 AI Summary")
if st.button("Generate Summary"):
if not clean_text:
st.warning("Chat is empty after cleaning. Check the file format.")
else:
with st.spinner("Analyzing..."):
start_time = time.time() # Latency Timer Start
# Generate
chunks = chunk_text(clean_text)
summary_parts = []
# Summarize first 3 chunks to keep it fast
for i, chunk in enumerate(chunks[:3]):
try:
res = summarizer(chunk, max_length=128, min_length=30, do_sample=False)
summary_parts.append(res[0]['summary_text'])
except Exception as e:
st.warning(f"Could not summarize chunk {i+1}: {e}")
final_summary = " ".join(summary_parts)
end_time = time.time() # Latency Timer End
latency = end_time - start_time
st.success(final_summary)
st.info(f"⏱️ Model Latency: {latency:.2f} seconds") |