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app.py

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+import gradio as gr
+from transformers import pipeline
+
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+def analyze_text(text):
+    summary = summarizer(text, max_length=150, min_length=40, do_sample=False)[0]["summary_text"]
+    follow_ups = [
+        "What assumptions are made?",
+        "What are potential counterarguments?",
+        "What are possible next research questions?"
+    ]
+    return summary, "\n".join(follow_ups)
+
+with gr.Blocks() as demo:
+    gr.Markdown("### 🧠 Clarity Agent\nPaste text or upload a file to get a summary and follow-up ideas.")
+
+    with gr.Row():
+        input_text = gr.Textbox(label="Paste text", lines=12)
+        file_upload = gr.File(label="...or upload .txt/.md", file_types=[".txt", ".md"])
+
+    submit = gr.Button("Analyze")
+    output_summary = gr.Textbox(label="Summary")
+    output_questions = gr.Textbox(label="Follow-up Questions")
+
+    def load_and_analyze(paste, file):
+        content = paste or (file.read().decode() if file else "")
+        return analyze_text(content) if content.strip() else ("No input provided", "")
+
+    submit.click(load_and_analyze, inputs=[input_text, file_upload], outputs=[output_summary, output_questions])
+
+demo.launch()

requirements.txt

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+gradio
+transformers
+torch