Update app.py

app.py

@@ -1,85 +1,57 @@
-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()
+# app.py
+
+import streamlit as st
+from modules.data_loader import load_texts
+from modules.preprocessing import preprocess_texts
+from modules.sentiment_analysis import analyze_sentiment
+from modules.summarizer import summarize_texts
+from modules.visualization import generate_wordcloud, plot_sentiment_distribution
+from modules.utils import save_results_to_csv, display_dataframe
+from config.settings import DATA_PATH
+
+# Title
+st.title("📊 Sentiment Analysis of E-Consultation Comments")
+st.write("AI-powered tool for sentiment analysis, summarization, and visualization.")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a .txt file (one comment per line)", type=["txt"])
+
+if uploaded_file:
+    # Save uploaded file
+    with open(DATA_PATH, "wb") as f:
+        f.write(uploaded_file.read())
+    st.success("✅ File uploaded successfully!")
+
+    # Load data
+    texts = load_texts(DATA_PATH)
+    st.write(f"Loaded **{len(texts)}** comments.")
+
+    # Preprocess
+    clean_texts = preprocess_texts(texts)
+
+    # Sentiment Analysis
+    st.subheader("🔍 Sentiment Analysis")
+    sentiment_results = analyze_sentiment(clean_texts)
+    sentiment_chart = plot_sentiment_distribution(sentiment_results)
+    st.image(sentiment_chart, caption="Sentiment Distribution")
+
+    # Summarization
+    st.subheader("📝 Summarization")
+    summaries = summarize_texts(clean_texts)
+    st.write("Here are some summaries:")
+    for s in summaries[:5]:
+        st.write(f"- {s['summary']}")
+
+    # Word Cloud
+    st.subheader("☁ Word Cloud")
+    wc_image = generate_wordcloud(clean_texts)
+    st.image(wc_image, caption="Word Cloud of Comments")
+
+    # Results Table
+    st.subheader("📑 Results Table")
+    df = display_dataframe(sentiment_results, summaries)
+    st.dataframe(df)
+
+    # Save results
+    results_path = save_results_to_csv(sentiment_results, summaries)
+    st.download_button("📥 Download Results CSV", data=open(results_path, "rb"), file_name="results.csv")