requirements.txt

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Simple keyword-based action classifier
+def classify_action(email_text):
+    email_lower = email_text.lower()
+    if "meeting" in email_lower or "schedule" in email_lower:
+        return "Schedule a meeting"
+    elif "question" in email_lower or "reply" in email_lower or "can you" in email_lower:
+        return "Reply"
+    elif "unsubscribe" in email_lower or "spam" in email_lower:
+        return "Delete or Mark as Spam"
+    else:
+        return "Read and Archive"
+
+# Main function
+def summarize_and_recommend(email_text):
+    if not email_text.strip():
+        return "No content provided.", "No action"
+
+    # Summarize
+    summary = summarizer(email_text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
+    
+    # Recommend action
+    action = classify_action(email_text)
+
+    return summary, action
+
+# Gradio UI
+iface = gr.Interface(
+    fn=summarize_and_recommend,
+    inputs=gr.Textbox(lines=15, placeholder="Paste your email content here..."),
+    outputs=[
+        gr.Textbox(label="Summary"),
+        gr.Textbox(label="Suggested Action")
+    ],
+    title="📩 Smart Email Summarizer & Action Recommender",
+    description="Paste an email to get a quick summary and an action suggestion. Uses Hugging Face's BART model for summarization.",
+    theme="default"
+)
+
+iface.launch()