Update app.py

app.py

@@ -1,53 +1,47 @@
 import gradio as gr
-from transformers import pipeline
-from wordcloud import WordCloud
-import matplotlib.pyplot as plt
-import io
-
-# Load Hugging Face pipelines
-sentiment_model = pipeline("sentiment-analysis")
-summarizer = pipeline("summarization")
-
-# Function to generate word cloud
-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()
-    return img.getvalue()
-
-# Core function
-def analyze_text(user_input):
-    # Sentiment
-    sentiment = sentiment_model(user_input)[0]
-
-    # Summary
-    try:
-        summary = summarizer(user_input, max_length=60, min_length=10, do_sample=False)[0]['summary_text']
-    except Exception:
-        summary = "Summary not available for very short text."
-
-    # Wordcloud
-    wc_img = generate_wordcloud(user_input)
-
-    return f"**Label:** {sentiment['label']} | **Score:** {sentiment['score']:.2f}", summary, wc_img
-
-
-# Gradio Interface
-demo = gr.Interface(
-    fn=analyze_text,
-    inputs=gr.Textbox(lines=5, placeholder="Enter stakeholder comment here..."),
-    outputs=[
-        gr.Textbox(label="Sentiment"),
-        gr.Textbox(label="Summary"),
-        gr.Image(label="Word Cloud")
-    ],
-    title="E-Consultation Sentiment Analysis",
-    description="Enter a comment/suggestion. The system predicts sentiment, generates a summary, and visualizes keywords."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+import pandas as pd
+from pipeline import process_comment
+
+def analyze_single(text):
+    sentiment, summary, wc_img = process_comment(text)
+    result = f"**Label:** {sentiment['label']} | **Score:** {sentiment['score']:.2f}"
+    return result, summary, wc_img
+
+def analyze_batch(file):
+    df = pd.read_csv(file.name)
+    results = []
+    summaries = []
+    combined_text = ""
+
+    for comment in df["comment"]:
+        sentiment, summary, _ = process_comment(comment)
+        results.append(f"{sentiment['label']} ({sentiment['score']:.2f})")
+        summaries.append(summary)
+        combined_text += " " + comment
+
+    # Generate one big word cloud for all comments
+    _, _, wc_img = process_comment(combined_text)
+
+    df["Sentiment"] = results
+    df["Summary"] = summaries
+
+    return df, wc_img
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🏛️ E-Consultation Sentiment Analysis\nAnalyze stakeholder comments with AI.")
+    
+    with gr.Tab("Single Comment"):
+        inp = gr.Textbox(lines=5, placeholder="Enter stakeholder comment...")
+        out1 = gr.Textbox(label="Sentiment")
+        out2 = gr.Textbox(label="Summary")
+        out3 = gr.Image(label="Word Cloud")
+        btn = gr.Button("Analyze")
+        btn.click(analyze_single, inputs=inp, outputs=[out1, out2, out3])
+
+    with gr.Tab("Batch Upload (CSV)"):
+        file_in = gr.File(label="Upload CSV with 'comment' column", type="file")
+        df_out = gr.Dataframe(label="Analysis Results")
+        wc_out = gr.Image(label="Word Cloud")
+        file_in.change(analyze_batch, inputs=file_in, outputs=[df_out, wc_out])
+
+demo.launch()