Update app.py

app.py

@@ -1,52 +1,111 @@
 import gradio as gr
-import pandas as pd
-from extract import extract_upi_transactions
-from clean import clean_upi_data
-from analysis import analyze_spending_pattern, get_financial_advice
-from visualize import plot_spending_by_category
+import PyPDF2
+import google.generativeai as genai
+import re
+import io
 
-def process_pdf(file):
-    """
-    Process the uploaded PDF UPI transaction statement.
-    Extracts, cleans, analyzes, and visualizes spending data.
-    """
+# 🔐 Gemini API Key
+GEMINI_API_KEY = "AIzaSyDnx_qUjGTFG1pv1otPUhNt_bGGv14aMDI"
+genai.configure(api_key=GEMINI_API_KEY)
+
+# 📄 Extract text from PDFdef extract_text_from_pdf(file):
     try:
-        # ✅ Step 1: Extract transactions
-        df = extract_upi_transactions(file.name)
+        reader = PyPDF2.PdfReader(file)
+        text = ""
+        for page in reader.pages:
+            content = page.extract_text()
+            if content:
+                text += content + "\n"
+        return text.strip()
+    except:
+        return ""
+
+def extract_section(full_text, label):
+    pattern = rf"\*\*\- {re.escape(label)}:\*\*\s*(.*?)(?=\n\*\*|\Z)"
+    match = re.search(pattern, full_text, re.DOTALL)
+    return match.group(1).strip() if match else "❓ Not found"
 
-        if df is None or df.empty:
-            return "Error: No transactions found in the uploaded PDF.", "", ""
+def analyze_financial_data(file):
+    text = extract_text_from_pdf(file)
+    if not text:
+        return ("⚠️ Failed to extract text. Ensure it’s not a scanned image PDF.", "", "", "", "", "", "", "", None)
 
-        # ✅ Step 2: Clean data
-        df = clean_upi_data(df)
+    prompt = f"""
+    Analyze the following Paytm transaction history and generate financial insights in the following structure:
+    **Financial Insights**
+    **- Monthly Income & Expenses:** [data]
+    **- Unnecessary Expense Categories:** [data]
+    **- Estimated Savings %:** [data]
+    **- Spending Trends:** [data]
+    **- Category-wise Expense Breakdown (Partial):** [data]
+    **- Cost Control Suggestions:** [data]
+    Transaction History:
+    {text}
+    """
 
-        # ✅ Step 3: Perform financial analysis
-        analysis = analyze_spending_pattern(df)
+    try:
+        model = genai.GenerativeModel("gemini-1.5-flash")
+        response = model.generate_content(prompt)
+        full_text = response.text.strip()
 
-        # ✅ Step 4: Generate financial advice
-        advice = get_financial_advice(df)
+        # Generate downloadable report
+        report_io = io.StringIO()
+        report_io.write(full_text)
+        report_bytes = io.BytesIO(report_io.getvalue().encode())
+        report_bytes.name = "financial_analysis.txt"
 
-        # ✅ Step 5: Generate spending visualization
-        plot_spending_by_category(df)
+        return (
+            "✅ Analysis Complete",
+            text[:2000] + "..." if len(text) > 2000 else text,  # Full text preview
+            extract_section(full_text, "Monthly Income & Expenses"),
+            extract_section(full_text, "Unnecessary Expense Categories"),
+            extract_section(full_text, "Estimated Savings %"),
+            extract_section(full_text, "Spending Trends"),
+            extract_section(full_text, "Category-wise Expense Breakdown (Partial)"),
+            extract_section(full_text, "Cost Control Suggestions"),
+            report_bytes
+        )
 
-        return df, analysis, advice
-    
     except Exception as e:
-        return f"Error: {str(e)}", "", ""
-
-# ✅ Gradio UI
-interface = gr.Interface(
-    fn=process_pdf,
-    inputs=gr.File(label="Upload UPI PDF Statement"),
-    outputs=[
-        gr.Dataframe(label="Processed Transaction Data"),
-        gr.Textbox(label="Spending Analysis"),
-        gr.Textbox(label="Financial Advice"),
-    ],
-    title="UPI Financial Analyzer",
-    description="Upload your UPI transaction statement to get insights, trends, and personalized recommendations.",
-    theme="compact",
-)
-
-if __name__ == "__main__":
-    interface.launch()
+        return (f"❌ Gemini Error: {e}", "", "", "", "", "", "", "", None)
+
+# 🎨 Gradio Interface
+with gr.Blocks(title="AI Finance Assistant") as demo:
+    gr.Markdown("## 💰 AI-Powered Personal Finance Assistant")
+    gr.Markdown("Upload your **Paytm transaction PDF** (text-based only) and get structured financial insights using **Gemini AI**.")
+
+    with gr.Row():
+        with gr.Column():
+            pdf_input = gr.File(label="📂 Upload PDF", file_types=[".pdf"])
+            analyze_btn = gr.Button("🔍 Analyze My Finances")
+
+        with gr.Column():
+            status = gr.Textbox(label="✅ Status", interactive=False)
+            download_btn = gr.File(label="📥 Download Report", interactive=False)
+
+    with gr.Accordion("📜 Extracted PDF Text (preview)", open=False):
+        pdf_text = gr.Textbox(lines=10, label="Extracted Text", interactive=False)
+
+    with gr.Row():
+        income_box = gr.Textbox(label="💵 Monthly Income & Expenses", lines=5, interactive=False)
+        unnecessary_box = gr.Textbox(label="🛒 Unnecessary Expenses", lines=5, interactive=False)
+
+    with gr.Row():
+        savings_box = gr.Textbox(label="💰 Estimated Savings %", lines=2, interactive=False)
+        trends_box = gr.Textbox(label="📈 Spending Trends", lines=5, interactive=False)
+
+    with gr.Row():
+        breakdown_box = gr.Textbox(label="📊 Category-wise Breakdown", lines=6, interactive=False)
+        suggestion_box = gr.Textbox(label="🧠 Cost Control Suggestions", lines=6, interactive=False)
+
+    analyze_btn.click(
+        fn=analyze_financial_data,
+        inputs=pdf_input,
+        outputs=[
+            status, pdf_text,
+            income_box, unnecessary_box, savings_box,
+            trends_box, breakdown_box, suggestion_box, download_btn
+        ]
+    )
+
+demo.launch()