Create app.py

app.py

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+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()