upload files

app.py

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+import gradio as gr
+import transformers
+from transformers import pipeline
+import tf_keras as keras
+import pandas as pd
+import tempfile
+import os
+
+# Load pre-trained spam classifier
+spam_classifier = pipeline(
+    "text-classification",
+    model="mrm8488/bert-tiny-finetuned-sms-spam-detection"
+)
+
+def classify_batch(file):
+    """Process uploaded CSV/TXT file with multiple emails"""
+    try:
+        results = []
+        
+        # Check if file exists
+        if not file.name:
+            raise gr.Error("No file uploaded")
+
+        # --- CSV File Handling ---
+        if file.name.endswith('.csv'):
+            df = pd.read_csv(file)
+            
+            # Check for required email column
+            if 'email' not in df.columns:
+                raise gr.Error("CSV file must contain a column named 'email'")
+                
+            emails = df['email'].tolist()
+
+        # --- Text File Handling ---
+        elif file.name.endswith('.txt'):
+            with open(file.name, 'r') as f:
+                emails = f.readlines()
+        
+        # --- Unsupported Format ---
+        else:
+            raise gr.Error("Unsupported file format. Only CSV/TXT accepted")
+
+        # Process emails (common for both formats)
+        emails = emails[:100]  # Limit to 100 emails
+        for email in emails:
+            # Handle empty lines in text files
+            if not email.strip():
+                continue
+                
+            prediction = spam_classifier(email.strip())[0]
+            results.append({
+                "email": email.strip()[:50] + "...",
+                "label": "SPAM" if prediction["label"] == "LABEL_1" else "HAM",
+                "confidence": f"{prediction['score']:.4f}"
+            })
+
+        return pd.DataFrame(results)
+
+    except gr.Error as e:
+        raise e  # Show pop-up for expected errors
+    except Exception as e:
+        raise gr.Error(f"Processing error: {str(e)}")
+
+def classify_text(text):
+    result = spam_classifier(text)[0]
+    return {
+        "Spam": result["score"] if result["label"] == "LABEL_1" else 1 - result["score"],
+        "Ham": result["score"] if result["label"] == "LABEL_0" else 1 - result["score"]
+    }
+
+with gr.Blocks(title="Spam Classifier Pro") as demo:
+    gr.Markdown("# 📧 Welcome to Spamedar!")
+    
+    
+    with gr.Tab("✉️ Single Email"):
+        gr.Interface(
+            description="<h2>Copy your email to find out if it's a is Spam or Ham👇<h2>",
+            fn=classify_text,
+            inputs=gr.Textbox(label="Input Email", lines=3),
+            outputs=gr.Label(label="Classification"),
+            examples=[
+                ["Urgent: Verify your account details now!"],
+                ["Hey, can we meet tomorrow to discuss the project?"],
+                ["WINNER! You've been selected for a $1000 Walmart Gift Card!"],
+                ["Your account needs verification. Click here to confirm your details."],
+                ["Meeting rescheduled to Friday 2 PM"]
+            ]
+        )
+    current_dir = os.getcwd()
+    with gr.Tab("📨 Multiple Emails"):
+        gr.Markdown("## Upload email batch (CSV or TXT)")
+        file_input = gr.File(label="Upload File", file_types=[".csv", ".txt"])
+        clear_btn = gr.Button("Clear Selection", variant="secondary")
+        output_table = gr.Dataframe(
+            headers=["email", "label", "confidence"],
+            datatype=["str", "str", "number"],
+            interactive=False,
+            label="Classification Results"
+        )
+        download_btn = gr.DownloadButton(label="Download Results")
+
+        def process_file(file):
+            """Process file and return (display_df, download_path)"""
+            try:
+                if file is None:
+                    return pd.DataFrame(), None
+
+                results_df = classify_batch(file)
+                with tempfile.NamedTemporaryFile(suffix=".csv", delete=False) as f:
+                    results_df.to_csv(f.name, index=False)
+                    return results_df, f.name
+            except Exception as e:
+                raise gr.Error(f"Error processing file: {str(e)}")
+
+        def clear_selection():
+            ###clear file input and results function
+            return None, pd.DataFrame(), None
+        
+        file_input.upload(
+            fn=process_file,
+            inputs=file_input,
+            outputs=[output_table, download_btn]  # Update both components
+        )
+
+        clear_btn.click(
+            fn=clear_selection,
+            outputs=[file_input, output_table, download_btn]
+        )
+
+        example_files= [
+            os.path.join(os.getcwd(), "sample_emails.csv"),
+            os.path.join(os.getcwd(), "batch_emails.txt"),
+        ]
+        if all(os.path.exists(f) for f in example_files):
+            gr.Examples(
+                examples=[[f] for f in example_files],
+                inputs=file_input,
+                outputs=[output_table, download_btn],
+                fn=process_file,
+                cache_examples=True,
+                label="Click any example below to test:"
+            )
+            
+        else:
+            print("Warning: Example files missing. Place these in your project root:")
+            print("- sample_emails.csv")
+            print("- batch_emails.txt")
+        
+if __name__ == "__main__":
+    demo.launch(share=True)