Update app.py

app.py

@@ -1,42 +1,42 @@
 import gradio as gr
-import torchaudio
-from transformers import pipeline
+import pdfplumber
+import pandas as pd
 
-# Load a voice cloning or TTS model
-# Here we use a placeholder for a voice cloning model like Tortoise-TTS
-# You can replace this with your preferred library
-def voice_cloning(input_audio, song_text, musician_style):
-    # Load the input audio (your voice)
-    waveform, sample_rate = torchaudio.load(input_audio)
+# Function to process PDF and classify transactions
+def process_pdf(file):
+    if file is None:
+        return "No file uploaded."
     
-    # Process the waveform to extract voice features (using Tortoise-TTS or similar)
-    # This is a placeholder - you'll need to use a real voice cloning pipeline here
-    cloned_voice = f"Processed your voice for song '{song_text}' in the style of {musician_style}"
-    
-    # Synthesize the song text using your cloned voice
-    # Combine with the musical style of the selected musician
-    synthesized_song = f"Singing '{song_text}' with your voice in the style of {musician_style}."
-    
-    return synthesized_song
+    # Extract text from the uploaded PDF
+    with pdfplumber.open(file.name) as pdf:
+        text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
 
-# Create a Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("### Voice Cloning & Singing in a Musician's Style")
-    
-    with gr.Row():
-        input_audio = gr.Audio(label="Upload Your Voice", type="filepath")
-        song_text = gr.Textbox(label="Enter Song Lyrics", placeholder="Enter the song lyrics here...")
-        musician_style = gr.Textbox(label="Enter Musician's Style", placeholder="e.g., Adele, Ed Sheeran, etc.")
-    
-    output = gr.Textbox(label="Synthesized Song")
-    
-    generate_button = gr.Button("Generate")
-    generate_button.click(
-        voice_cloning, 
-        inputs=[input_audio, song_text, musician_style], 
-        outputs=output
-    )
+    # Extract transactions (Modify based on statement format)
+    lines = text.split("\n")
+    transactions = [line for line in lines if any(char.isdigit() for char in line)]  
+
+    # Convert to DataFrame
+    df = pd.DataFrame([line.split()[:3] for line in transactions], columns=["Date", "Description", "Amount"])
+
+    # Classification function (Modify as needed)
+    def classify_transaction(description):
+        categories = {
+            "Grocery": ["Walmart", "Kroger", "Whole Foods"],
+            "Dining": ["McDonald's", "Starbucks", "Chipotle"],
+            "Bills": ["Verizon", "AT&T", "Con Edison"],
+            "Entertainment": ["Netflix", "Spotify", "Amazon Prime"],
+            "Transport": ["Uber", "Lyft", "MetroCard"],
+        }
+        for category, keywords in categories.items():
+            if any(keyword in description for keyword in keywords):
+                return category
+        return "Other"
+
+    # Apply classification
+    df["Category"] = df["Description"].apply(classify_transaction)
 
-# Launch the app
-demo.launch()
+    return df  # Display the table
 
+# Gradio Interface
+app = gr.Interface(fn=process_pdf, inputs=gr.File(type="file"), outputs="dataframe", title="Bank Statement Classifier")
+app.launch()