Add application file

app.py

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+
+import gradio as gr
+from transformers import AutoModelForCausalLM, pipeline
+from PIL import Image
+import pandas as pd
+import pytesseract
+
+# Load models
+text_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)
+tts_pipeline = pipeline("text-to-speech", model="parler-tts/parler-tts-large-v1")
+
+# Function to process PDF files
+def process_pdf(pdf):
+    text = ""
+    # Assuming each page in the PDF is processed into text
+    for page in pdf.pages:
+        text += pytesseract.image_to_string(page)
+    return text
+
+# Function to process CSV files
+def process_csv(csv):
+    df = pd.read_csv(csv)
+    return df.to_string()
+
+# Function to process images
+def process_image(image):
+    return pytesseract.image_to_string(image)
+
+# Main function that handles all file types
+def handle_files(file):
+    if file.name.endswith('.pdf'):
+        text = process_pdf(file)
+    elif file.name.endswith('.csv'):
+        text = process_csv(file)
+    else:
+        image = Image.open(file)
+        text = process_image(image)
+    
+    # Generate audio from the text
+    audio = tts_pipeline(text)
+    
+    return text, audio["audio"]
+
+# Gradio interface
+demo = gr.Interface(
+    fn=handle_files,
+    inputs=gr.File(type=["pdf", "csv", "image"]),
+    outputs=[gr.Textbox(label="Extracted Text"), gr.Audio(label="Generated Audio")],
+    title="AuditBidden - Public Procurement Auditor"
+)
+
+if __name__ == "__main__":
+    demo.launch()