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9b3510a 534ee48 ec05904 534ee48 ec05904 534ee48 1eb3e75 534ee48 ec05904 534ee48 ec05904 534ee48 ec05904 534ee48 ec05904 534ee48 ec05904 534ee48 ec05904 534ee48 ec05904 534ee48 ec05904 534ee48 1eb3e75 534ee48 ec05904 534ee48 | 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 | import gradio as gr
from transformers import pipeline
from wordcloud import WordCloud
import matplotlib
matplotlib.use("Agg") # fix for HF Spaces
import matplotlib.pyplot as plt
import io
import pandas as pd
# Load Hugging Face pipelines with smaller stable models
sentiment_model = pipeline(
"sentiment-analysis",
model="distilbert-base-uncased-finetuned-sst-2-english"
)
summarizer = pipeline(
"summarization",
model="sshleifer/distilbart-cnn-12-6"
)
# Word cloud generator
def generate_wordcloud(text):
wordcloud = WordCloud(width=800, height=400, background_color="white").generate(text)
img = io.BytesIO()
plt.figure(figsize=(8, 4))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.savefig(img, format="png")
plt.close()
img.seek(0)
return img
# Process single comment
def process_comment(comment):
# Sentiment
sentiment = sentiment_model(comment)[0]
# Summary
try:
summary = summarizer(comment, max_length=60, min_length=10, do_sample=False)[0]['summary_text']
except Exception:
summary = "Summary not available for short text."
# Word cloud
wc_img = generate_wordcloud(comment)
return f"{sentiment['label']} ({sentiment['score']:.2f})", summary, wc_img
# Batch processing from CSV
def analyze_batch(file_path):
df = pd.read_csv(file_path)
results, summaries = [], []
combined_text = ""
for comment in df["comment"].astype(str):
sentiment, summary, _ = process_comment(comment)
results.append(sentiment)
summaries.append(summary)
combined_text += " " + comment
# One word cloud for all comments
wc_img = generate_wordcloud(combined_text)
df["Sentiment"] = results
df["Summary"] = summaries
return df, wc_img
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 📝 E-Consultation Sentiment Analysis")
with gr.Tab("Single Comment"):
inp = gr.Textbox(label="Enter Comment")
out1 = gr.Textbox(label="Sentiment")
out2 = gr.Textbox(label="Summary")
out3 = gr.Image(label="Word Cloud")
inp.submit(process_comment, inp, [out1, out2, out3])
with gr.Tab("Batch Upload"):
file_in = gr.File(label="Upload CSV with 'comment' column", type="filepath")
out_df = gr.Dataframe(label="Results")
out_wc = gr.Image(label="Word Cloud (All Comments)")
file_in.change(analyze_batch, file_in, [out_df, out_wc])
if __name__ == "__main__":
demo.launch()
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