File size: 27,175 Bytes
2b0414b cd29e0d 2b0414b a23d62f 2b0414b a23d62f 2b0414b 792826f 2b0414b 792826f 2b0414b a23d62f 2b0414b a23d62f 2b0414b cd29e0d 2b0414b cd29e0d 2b0414b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 |
from flask import Flask, request, jsonify, send_from_directory
import pandas as pd
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from wordcloud import WordCloud
import uuid
import io
import base64
import os
from PIL import Image
app = Flask(__name__)
UPLOAD_FOLDER = "uploads"
# os.makedirs(UPLOAD_FOLDER, exist_ok=True)
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
# Load model and tokenizer once
tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased")
model_path = "./src/emotion_final_model"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BertForSequenceClassification.from_pretrained(model_path).to(device)
model.eval()
# Label Mapping
label_mapping = {0: "negative", 1: "neutral", 2: "positive"}
@app.route('/predict', methods=['POST'])
def predict():
data = request.get_json()
text = data.get('text')
if not text:
return jsonify({"error": "No text provided"}), 400
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
inputs = {key: value.to(device) for key, value in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = torch.argmax(logits, dim=-1).item()
sentiment = label_mapping[predicted_class_idx]
return jsonify({"sentiment": sentiment})
aspect_keywords = {
"Quality": ["quality", "material", "durable", "performance", "sturdy", "broken", "defective", "معیار", "ٹوٹا ہوا", "خراب"],
"Price": ["price", "cheap", "expensive", "value", "cost", "قیمت", "مہنگا", "سستا", "قیمت زیادہ"],
"Delivery": ["delivery", "shipping", "arrived", "late", "courier", "ترسیل", "شپنگ", "تاخیر", "دیر سے پہنچا"],
"Usability": ["easy to use", "setup", "installation", "instructions", "user-friendly", "آسان", "استعمال میں آسان", "سیٹ اپ", "تنصیب"],
"Design": ["design", "style", "appearance", "color", "looks", "ڈیزائن", "خوبصورتی", "رنگ", "ساخت"],
"Warranty/Support": ["warranty", "support", "return", "replacement", "service center", "وارنٹی", "واپسی", "تبادلہ", "سروس سینٹر"]
}
def detect_aspects(text):
text_lower = text.lower()
detected = []
for aspect, keywords in aspect_keywords.items():
if any(keyword in text_lower for keyword in keywords):
detected.append(aspect)
return detected
@app.route("/analyze", methods=["POST"])
def analyze():
if 'file' not in request.files:
return jsonify({"error": "No file uploaded"}), 400
file = request.files['file']
print(file.filename)
df = pd.read_csv(file)
print(df.to_string())
total_positive = 0
total_negative = 0
total_neutral = 0
all_text = ""
# Aspect summary
aspect_summary = {aspect: {"positive": 0, "negative": 0, "neutral": 0, "total": 0} for aspect in aspect_keywords}
for text in df['Review'].dropna():
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
predicted_class_idx = torch.argmax(outputs.logits, dim=-1).item()
sentiment = label_mapping[predicted_class_idx]
if sentiment == "positive":
total_positive += 1
elif sentiment == "negative":
total_negative += 1
else:
total_neutral += 1
all_text += " " + text
detected_aspects = detect_aspects(text)
for aspect in detected_aspects:
aspect_summary[aspect][sentiment] += 1
aspect_summary[aspect]["total"] += 1
# Generate WordCloud
wordcloud = WordCloud(width=800, height=400, background_color='white', font_path='src/urdu_font.ttf').generate(all_text)
# Save in uploads folder
# if not os.path.exists("uploads"):
# os.makedirs("uploads")
wordcloud_path = os.path.join("uploads", f"wordcloud{uuid.uuid4()}.png")
wordcloud.to_file(wordcloud_path)
# Convert image to base64
with open(wordcloud_path, "rb") as image_file:
encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
print({
"total_positive": total_positive,
"total_negative": total_negative,
"total_neutral": total_neutral,
"aspect_summary": aspect_summary,
"wordcloud_image_path": wordcloud_path,
})
return jsonify({
"total_positive": total_positive,
"total_negative": total_negative,
"total_neutral": total_neutral,
"aspect_summary": aspect_summary,
"wordcloud_image_path": wordcloud_path,
# "wordcloud_image_base64": encoded_image
})
def run_flask():
app.run(host="0.0.0.0", port=5000)
import threading
threading.Thread(target=run_flask).start()
import streamlit as st
import pandas as pd
import plotly.express as px
from io import BytesIO, StringIO
from PIL import Image
import random
import requests
import os
import uuid
import tempfile
API_URL = 'http://127.0.0.1:5000/analyze'
# -------------------
# PAGE CONFIG & THEME
# -------------------
st.set_page_config(
page_title="Multilingual Sentiment Analyzer",
layout="wide"
)
st.markdown("""
<style>
/* Light theme override */
html, body, .stApp {
background-color: #ffffff !important;
color: #000000 !important;
}
h1, h2, h3, h4, h5, h6, p, div, span, label, section, .markdown-text-container {
color: #000000 !important;
}
.stFileUploader > div, .stFileUploader div div {
background-color: #f9f9f9 !important;
border: 1px solid #ccc !important;
color: #000000 !important;
}
</style>
""", unsafe_allow_html=True)
st.markdown("""
<div style='text-align: center; padding-top: 10px;'>
<h1 style='font-size: 40px;'>🌍 Multilingual Sentiment Analysis Dashboard</h1>
<p style='font-size: 18px; color: #ccc; max-width: 720px; margin: auto;'>
Upload a CSV to explore sentiment Report. With sentiment analysis, you can catch early signals, reduce risk, and validate market fit — even across global audiences.
</p>
</div>
""", unsafe_allow_html=True)
# -------------------
# DUMMY DATA FUNCTION
# -------------------
def load_dummy_data():
return pd.DataFrame({
"Review": [
"La livraison était très rapide et le service excellent.",
"The product quality was terrible, I want a refund.",
"Servicio al cliente fue amable pero no resolvieron mi problema.",
"Das Produkt kam beschädigt an und der Support war unhöflich.",
"Great value for the price, I'm very happy!",
"Muy mal embalaje, pero el envío fue rápido.",
"客服很好,但产品描述不准确。",
"Perfect fit, just as described. Will buy again!"
]
})
# -------------------
# MAIN UPLOAD BLOCK (VISIBLE)
# -------------------
with st.expander("📁 Upload Your CSV File", expanded=True):
uploaded_file = st.file_uploader("Choose a CSV file with reviews", type=["csv"])
# Analysis button moved here, right after file upload
run_analysis = st.button("🚀 Run Analysis", type="primary")
# Load Data: Uploaded or Dummy
if uploaded_file:
try:
# Read uploaded CSV file
df = pd.read_csv(uploaded_file)
if df.empty:
st.error("The uploaded CSV file is empty.")
df = load_dummy_data()
else:
st.success("✅ File uploaded successfully!")
except Exception as e:
st.error(f"Error reading CSV: {e}")
df = load_dummy_data()
else:
st.info("Using built-in demo data. Upload a CSV to use your own.")
df = load_dummy_data()
# Preview data
st.write("✅ App is running! Here's a sample:")
st.dataframe(df.head())
# Column selection
# text_column = st.selectbox("📝 Select the column with review text:", df.columns)
# enable_aspect = st.checkbox("🔍 Include Aspect Report (Optional)", value=True)
text_column = 'Review'
enable_aspect = True
# -------------------
# SENTIMENT METRICS
# -------------------
st.markdown("---")
st.markdown("### 🔎 Sentiment Analysis Results")
# MOCK SENTIMENT PREDICTION
def fake_sentiment_predict(text):
return random.choice(["Positive", "Negative", "Neutral"]), round(random.uniform(0.65, 0.99), 2)
# Store the analyzed dataframe in session state
if 'analyzed_df' not in st.session_state:
st.session_state.analyzed_df = df.copy()
# Initialize variables
positive, negative, neutral, total = 0, 0, 0, 0
# Process the data when the Run Analysis button is clicked
if run_analysis:
if not enable_aspect:
# Use fake predictions if not calling the API
fake_results = [fake_sentiment_predict(text) for text in df[text_column]]
sentiments, confidences = zip(*fake_results)
# Update the analyzed dataframe
st.session_state.analyzed_df = df.copy()
st.session_state.analyzed_df["Sentiment"] = sentiments
st.session_state.analyzed_df["Confidence"] = confidences
# Count sentiment
sentiment_counts = pd.Series(sentiments).value_counts()
positive = sentiment_counts.get("Positive", 0)
negative = sentiment_counts.get("Negative", 0)
neutral = sentiment_counts.get("Neutral", 0)
total = positive + negative + neutral
else:
# API Call will be made - handled below
pass
def percent(part):
return f"{round((part / total) * 100)}%" if total else "0%"
# Layout: Cards + Chart
card_col, chart_col = st.columns([1.2, 2])
with card_col:
st.markdown("""
<style>
.card-container {
max-width: 10px;
margin: 0 auto;
}
.card {
padding: 10px;
border-radius: 12px;
margin-bottom: 10px;
font-size: 16px;
font-weight: 500;
line-height: 1.5;
background-color: var(--secondary-background-color);
border: 1px solid rgba(255,255,255,0.15);
color: white;
text-align: center;
}
.card strong {
font-size: 20px;
display: block;
margin-top: 5px;
}
</style>
<div class="card-container">
""", unsafe_allow_html=True)
if total > 0: # Only display chart if we have data
with chart_col:
fig = px.pie(
names=["Positive", "Negative", "Neutral"],
values=[positive, negative, neutral],
color_discrete_map={
"Positive": "#66bb6a",
"Negative": "#ef5350",
"Neutral": "#42a5f5"
}
)
fig.update_traces(
textinfo='percent+label',
hoverinfo='label+percent+value',
pull=[0.03, 0.03, 0.03]
)
fig.update_layout(
margin=dict(t=20, b=20, l=10, r=10),
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="white"
)
st.plotly_chart(fig, use_container_width=True)
# -------------------
# ASPECT REPORT (API Call)
# -------------------
if run_analysis and enable_aspect:
st.subheader("📌 Aspect Sentiment Summary")
with st.spinner("Fetching Aspect Report from API..."):
try:
# Create a dataframe with only the text column
api_df = df.copy()
# Create a unique filename with UUID
unique_filename = f"temp_reviews_{uuid.uuid4()}.csv"
# Write to a physical temporary file with UUID in name
with tempfile.NamedTemporaryFile(delete=False, suffix=unique_filename) as tmp:
api_df.to_csv(tmp.name, index=False)
tmp_file_path = tmp.name
# Open the file in binary mode
with open(tmp_file_path, 'rb') as file:
# Send the actual file
files = {'file': (unique_filename, file, 'text/csv')}
response = requests.post(API_URL, files=files)
# Clean up temporary file
try:
os.unlink(tmp_file_path)
except:
pass # Silently fail if we can't delete the temp file
if response.status_code == 200:
response_json = response.json()
# Store API response in session state
st.session_state.api_response_json = response_json
# Update metrics from API response
positive = response_json.get("total_positive", 0)
negative = response_json.get("total_negative", 0)
neutral = response_json.get("total_neutral", 0)
total = positive + negative + neutral
# Calculate percentages for Excel report if not provided by API
if "positive_percentage" not in response_json and total > 0:
response_json["positive_percentage"] = round((positive / total) * 100)
response_json["negative_percentage"] = round((negative / total) * 100)
response_json["neutral_percentage"] = round((neutral / total) * 100)
# Add total reviews to response_json if not present
if "total_reviews" not in response_json:
response_json["total_reviews"] = total
# Update metrics from API response
positive = response_json.get("total_positive", 0)
negative = response_json.get("total_negative", 0)
neutral = response_json.get("total_neutral", 0)
total = positive + negative + neutral
# Update the metrics cards with new data
with card_col:
st.markdown(f"""
<div class="card" style="border-color:#bfbfbf;">
📊 <strong>Total Reviews</strong>
{total}
</div>
<div class="card" style="border-color:#66bb6a;">
✅ <strong>{positive} Positive</strong>
{percent(positive)} of total
</div>
<div class="card" style="border-color:#ef5350;">
❗ <strong>{negative} Negative</strong>
{percent(negative)} of total
</div>
<div class="card" style="border-color:#42a5f5;">
😐 <strong>{neutral} Neutral</strong>
{percent(neutral)} of total
</div>
</div>
""", unsafe_allow_html=True)
# Update the pie chart
with chart_col:
fig = px.pie(
names=["Positive", "Negative", "Neutral"],
values=[positive, negative, neutral],
color_discrete_map={
"Positive": "#66bb6a",
"Negative": "#ef5350",
"Neutral": "#42a5f5"
}
)
fig.update_traces(
textinfo='percent+label',
hoverinfo='label+percent+value',
pull=[0.03, 0.03, 0.03]
)
fig.update_layout(
margin=dict(t=20, b=20, l=10, r=10),
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="white"
)
st.plotly_chart(fig, use_container_width=True)
# Update the analyzed dataframe with sentiment results from API
if "review_details" in response_json:
# Create a new dataframe from the API results
api_result_df = pd.DataFrame(response_json["review_details"])
# Store it in session state
st.session_state.analyzed_df = api_result_df
else:
# If review_details not provided, create basic sentiment columns
st.session_state.analyzed_df = df.copy()
# Try to extract sentiments if available in the API response
if "sentiments" in response_json:
st.session_state.analyzed_df["Sentiment"] = response_json["sentiments"]
# Add any other available result fields
for key in ["confidences", "languages"]:
if key in response_json:
column_name = key.rstrip("s").capitalize() # Convert "confidences" to "Confidence"
st.session_state.analyzed_df[column_name] = response_json[key]
# Prepare aspect DataFrame
aspect_rows = []
for aspect, values in response_json["aspect_summary"].items():
aspect_rows.append({
"Aspect": aspect,
"Positive": values["positive"],
"Negative": values["negative"],
"Neutral": values["neutral"],
"Total": values["total"]
})
aspect_df = pd.DataFrame(aspect_rows)
# Display aspect data if we have any
if not aspect_df.empty and aspect_df["Total"].sum() > 0:
# Store aspect dataframe in session state
st.session_state.aspect_dataframe = aspect_df
st.dataframe(aspect_df)
# Prepare data for bar chart
melted = aspect_df.melt(
id_vars="Aspect",
value_vars=["Positive", "Negative", "Neutral"],
var_name="Sentiment",
value_name="Count"
)
col1, col2 = st.columns([4, 2])
with col1:
st.markdown("### 📊 Sentiment by Aspect")
bar_chart = px.bar(
melted,
x="Aspect",
y="Count",
color="Sentiment",
barmode="group",
title=None,
color_discrete_map={
"Positive": "#66bb6a",
"Negative": "#ef5350",
"Neutral": "#42a5f5"
}
)
# Update chart theme for dark mode
bar_chart.update_layout(
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="white",
xaxis=dict(gridcolor="rgba(255,255,255,0.1)"),
yaxis=dict(gridcolor="rgba(255,255,255,0.1)")
)
st.plotly_chart(bar_chart, use_container_width=True)
with col2:
st.markdown("### 🌀 Review Keywords")
# Try to display wordcloud from API
if "wordcloud_image_base64" in response_json:
import base64
st.markdown("<div style='padding-top:60px'></div>", unsafe_allow_html=True)
st.image(
BytesIO(base64.b64decode(response_json["wordcloud_image_base64"])),
caption="Keyword Cloud",
use_container_width=True
)
else:
try:
# Try local wordcloud file as fallback
wordcloud_path = response_json.get("wordcloud_image_path")
if wordcloud_path and os.path.exists(wordcloud_path):
image = Image.open(wordcloud_path)
st.markdown("<div style='padding-top:60px'></div>", unsafe_allow_html=True)
st.image(image, caption="Keywords", use_container_width=True)
else:
# Try default wordcloud
if os.path.exists("wordcloud.jpg"):
image = Image.open("wordcloud.jpg")
st.markdown("<div style='padding-top:60px'></div>", unsafe_allow_html=True)
st.image(image, caption="Keywords", use_container_width=True)
except Exception as e:
st.warning(f"⚠ Word cloud image not found: {e}")
else:
st.info("No aspects detected in the reviews.")
else:
st.error(f"API Error: {response.status_code} - {response.text}")
except Exception as e:
st.error(f"API call failed: {e}")
import traceback
st.code(traceback.format_exc(), language="python")
# -------------------
# DOWNLOAD BUTTON
# -------------------
if run_analysis or total > 0:
st.subheader("📥 Download Analyzed File")
def generate_excel_report(df, aspect_data=None, response_json=None):
output = BytesIO()
with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
# Sheet 1: Main sentiment results
df.to_excel(writer, index=False, sheet_name='Sentiment_Report')
# Sheet 2: Aspect analysis (if available)
if aspect_data is not None and not aspect_data.empty:
aspect_data.to_excel(writer, index=False, sheet_name='Aspect_Analysis')
# Sheet 3: Summary stats (if API response available)
if response_json:
# Create a summary dataframe
summary_data = {
'Metric': ['Total Reviews', 'Positive', 'Negative', 'Neutral'],
'Count': [
response_json.get('total_reviews', 0),
response_json.get('total_positive', 0),
response_json.get('total_negative', 0),
response_json.get('total_neutral', 0)
],
'Percentage': [
'100%',
f"{response_json.get('positive_percentage', 0)}%",
f"{response_json.get('negative_percentage', 0)}%",
f"{response_json.get('neutral_percentage', 0)}%"
]
}
summary_df = pd.DataFrame(summary_data)
summary_df.to_excel(writer, index=False, sheet_name='Summary')
# Add any other relevant data from API response
if 'review_details' in response_json:
details_df = pd.DataFrame(response_json['review_details'])
details_df.to_excel(writer, index=False, sheet_name='Review_Details')
# Get workbook and add some formatting
workbook = writer.book
# Add formatting
header_format = workbook.add_format({
'bold': True,
'text_wrap': True,
'valign': 'top',
'border': 1
})
# Apply formatting to each worksheet safely
for sheet_name in writer.sheets:
worksheet = writer.sheets[sheet_name]
# Get column names from the DataFrame based on sheet name
if sheet_name == 'Sentiment_Report':
columns = df.columns
elif sheet_name == 'Aspect_Analysis' and aspect_data is not None:
columns = aspect_data.columns
elif sheet_name == 'Summary':
columns = summary_data.keys()
elif sheet_name == 'Review_Details' and 'review_details' in response_json:
columns = details_df.columns
else:
continue
# Write headers with formatting
for col_num, value in enumerate(columns):
worksheet.write(0, col_num, value, header_format)
# Auto-adjust columns' width (supported in newer versions)
try:
worksheet.autofit()
except AttributeError:
# Fallback for older xlsxwriter versions
for col_num, value in enumerate(columns):
# Set width based on header content
worksheet.set_column(col_num, col_num, max(10, len(str(value)) + 2))
return output.getvalue()
# Store API response in session state to access it for download
if 'api_response_json' not in st.session_state:
st.session_state.api_response_json = None
if 'aspect_dataframe' not in st.session_state:
st.session_state.aspect_dataframe = None
# Update these values when API response is received
if run_analysis and enable_aspect and 'response_json' in locals():
st.session_state.api_response_json = response_json
if 'aspect_df' in locals() and not aspect_df.empty:
st.session_state.aspect_dataframe = aspect_df
st.download_button(
label="📥 Download Results as Excel",
data=generate_excel_report(
st.session_state.analyzed_df, # Use the analyzed dataframe instead of original df
st.session_state.aspect_dataframe,
st.session_state.api_response_json
),
file_name="sentiment_analysis_report.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
# Add a footer with dark theme
st.markdown("""
<div style="text-align: center; margin-top: 50px; padding: 20px; color: #888; font-size: 14px;">
<p>Multilingual Sentiment Analysis Dashboard | Made with Streamlit</p>
</div>
""", unsafe_allow_html=True) |