Update app.py

app.py

@@ -1,25 +1,118 @@
-import numpy as np
 import gradio as gr
+import openpyxl
+import PyPDF2
+import pandas as pd
+from PIL import Image
+import pytesseract  # Replaced EasyOCR
+import io
+import os
+from huggingface_hub import InferenceClient
 
-notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
+# Access the Hugging Face token from the environment variable
+hf_token = os.environ.get("HF_TOKEN")
 
+def reconcile_statements(erp_file, bank_file):
+    yield "⏳ Processing your request... Please wait.", ""
 
-def generate_tone(note, octave, duration):
-    sr = 48000
-    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
-    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
-    duration = int(duration)
-    audio = np.linspace(0, duration, duration * sr)
-    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
-    return (sr, audio)
+    # your existing code block...
 
+    try:
+        # File parsing...
+        # Extract ERP statement
+        erp_statement = ""
+        erp_filename = erp_file.name
 
-gr.Interface(
-    generate_tone,
-    [
-        gr.Dropdown(notes, type="index"),
-        gr.Slider(minimum=4, maximum=6, step=1),
-        gr.Number(value=1, label="Duration in seconds"),
-    ],
-    "audio",
-).launch()
+        if erp_filename.endswith((".xlsx", ".xls")):
+            workbook = openpyxl.load_workbook(erp_filename)
+            sheet = workbook.active
+            for row in sheet.iter_rows():
+                for cell in row:
+                    erp_statement += str(cell.value) + "\t"
+                erp_statement += "\n"
+        elif erp_filename.endswith(".pdf"):
+            pdf_reader = PyPDF2.PdfReader(erp_filename)
+            for page in pdf_reader.pages:
+                erp_statement += page.extract_text() or ""
+        elif erp_filename.endswith((".jpg", ".jpeg", ".png")):
+            image = Image.open(io.BytesIO(erp_file.read()))
+            erp_statement = pytesseract.image_to_string(image)  # Tesseract OCR
+        elif erp_filename.endswith(".csv"):
+            df = pd.read_csv(erp_filename)
+            erp_statement = df.to_string()
+        else:
+            raise ValueError("Unsupported ERP file format.")
+
+        # Extract bank statement (similar logic as above)
+        bank_statement = ""
+        bank_filename = bank_file.name
+
+        if bank_filename.endswith((".xlsx", ".xls")):
+            workbook = openpyxl.load_workbook(bank_filename)
+            sheet = workbook.active
+            for row in sheet.iter_rows():
+                for cell in row:
+                    bank_statement += str(cell.value) + "\t"
+                bank_statement += "\n"
+        elif bank_filename.endswith(".pdf"):
+            pdf_reader = PyPDF2.PdfReader(bank_filename)
+            for page in pdf_reader.pages:
+                bank_statement += page.extract_text() or ""
+        elif bank_filename.endswith((".jpg", ".jpeg", ".png")):
+            image = Image.open(io.BytesIO(bank_file.read()))
+            bank_statement = pytesseract.image_to_string(image)  # Tesseract OCR
+        elif bank_filename.endswith(".csv"):
+            df = pd.read_csv(bank_filename)
+            bank_statement = df.to_string()
+        else:
+            raise ValueError("Unsupported bank file format.")
+
+        # Hugging Face request...
+        prompt = f"Reconcile these statements:\nERP:\n{erp_statement}\nBank:\n{bank_statement}"
+
+        client = InferenceClient(provider="together", api_key=hf_token)
+        completion = client.chat.completions.create(
+            model="deepseek-ai/DeepSeek-R1",
+            messages=[{"role": "user", "content": prompt}],
+        )
+
+        if completion.choices:
+            reconciliation_results = completion.choices[0].message.get('content', '')
+        else:
+            reconciliation_results = "⚠️ No response received from the model."
+
+        output = f"""
+        <div style="font-family: 'Segoe UI', ...">
+            <h2>🔍 Reconciliation Results</h2>
+            <div style="...">
+                <pre>{reconciliation_results}</pre>
+            </div>
+        </div>
+        """
+        yield "✅ Processing complete!", output
+
+    except Exception as e:
+        yield f"❌ Error: {e}", f"<h1>Error</h1><p>{e}</p>"
+
+with gr.Blocks(css="""
+#company-logo {
+    width: 25%;
+    margin: auto;
+    display: block;
+}
+""") as iface:
+    gr.Image("logo_Icon.png", elem_id="company-logo", label="Beiing Human")
+    status_text = gr.Markdown("👋 Upload your files to begin reconciliation.")
+    with gr.Row():
+        erp_input = gr.File(label="📂 Upload ERP Statement", type="filepath")
+        bank_input = gr.File(label="📂 Upload Bank Statement", type="filepath")
+    submit_btn = gr.Button("🔄 Start Reconciliation")
+    result_output = gr.HTML()
+
+    submit_btn.click(
+        fn=reconcile_statements,
+        inputs=[erp_input, bank_input],
+        outputs=[status_text, result_output]
+    )
+    
+if __name__ == "__main__":
+    iface.launch(debug=True)