init Gradio on HF Space

receipt_ai_platform/.env

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+TESSERACT_CMD=/usr/bin/tesseract
+GEMINI_API_KEY=AIzaSyBLjUx6iR8EYICDH_luidnGA7vRQntljgA

receipt_ai_platform/Dockerfile

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+FROM python:3.10-slim
+
+RUN apt-get update && apt-get install -y \
+    tesseract-ocr \
+    libtesseract-dev \
+    libleptonica-dev \
+    pkg-config \
+    poppler-utils \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app/main.py"]

receipt_ai_platform/app/db.py

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+import sqlite3
+from typing import List, Dict, Any
+
+DB_NAME = "receipts.db"
+
+CREATE_TABLE_QUERY = """
+CREATE TABLE IF NOT EXISTS receipts (
+    id INTEGER PRIMARY KEY,
+    item TEXT,
+    quantity INTEGER,
+    unit_price REAL,
+    price REAL,
+    merchant TEXT,
+    date TEXT
+);
+"""
+
+INSERT_RECEIPT_QUERY = """
+INSERT INTO receipts (item, quantity, unit_price, price, merchant, date)
+VALUES (?, ?, ?, ?, ?, ?);
+"""
+
+
+def init_db(db_name: str = DB_NAME) -> None:
+    with sqlite3.connect(db_name) as conn:
+        conn.execute(CREATE_TABLE_QUERY)
+
+
+def insert_receipt(
+    items: List[Dict[str, Any]], merchant: str, date: str, db_name: str = DB_NAME
+) -> None:
+    values = [
+        (i["item"], i["quantity"], i["unit_price"], i["price"], merchant, date)
+        for i in items
+    ]
+    with sqlite3.connect(db_name) as conn:
+        conn.executemany(INSERT_RECEIPT_QUERY, values)

receipt_ai_platform/app/main.py

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+import gradio as gr
+from ocr import extract_receipt_text, parse_receipt
+from db import init_db, insert_receipt
+from nlp import run_sql
+from datetime import datetime
+
+init_db()
+
+
+def upload_receipt(path: str) -> dict:
+    text = extract_receipt_text(path)
+    data = parse_receipt(text)
+    insert_receipt(
+        items=data["items"],
+        merchant=data["merchant"],
+        date=data["date"] or datetime.now().strftime("%d-%m-%Y")
+    )
+    return data
+
+
+def ask_ai(question: str) -> str:
+    if question.strip():
+        results = run_sql(question)
+        return results
+    return "Please add the question first."
+
+
+def ask_ai_with_status(question: str):
+    yield "Processing your question..."
+    result = run_sql(question)
+    yield result
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Upload your food receipt")
+    with gr.Row():
+        img_input = gr.Image(type="filepath")
+        output = gr.JSON()
+    upload_btn = gr.Button("Upload")
+    upload_btn.click(upload_receipt, inputs=img_input, outputs=output)
+
+    gr.Markdown("## Ask about your receipts")
+    question_input = gr.Textbox(label="Ask a question")
+    answer_output = gr.Markdown()
+    ask_btn = gr.Button("Ask")
+    ask_btn.click(ask_ai_with_status, inputs=question_input, outputs=answer_output)
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

receipt_ai_platform/app/nlp.py

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+import sqlite3
+import textwrap
+from dotenv import load_dotenv
+from google import genai
+from google.genai import types
+from db import DB_NAME
+
+load_dotenv()
+MODEL_NAME = "gemini-1.5-flash"
+
+client = genai.Client()
+
+SCHEMA = textwrap.dedent("""
+    CREATE TABLE receipts (
+        id INTEGER PRIMARY KEY,
+        item TEXT,
+        quantity INTEGER,
+        unit_price REAL,
+        price REAL,
+        date TEXT,
+        merchant TEXT
+    );
+""")
+
+EXAMPLES = textwrap.dedent("""
+    Example rows in receipts table:
+    id: 1, item: Grilled chicken sandwich, quantity: 2, unit_price: 8.5, price: 17.0, date: 11-04-2025, merchant: Your Company Inc.
+    id: 2, item: Caesar salad, quantity: 1, unit_price: 7.0, price: 7.0, date: 11-04-2025, merchant: Your Company Inc.
+    id: 3, item: Soft drinks, quantity: 3, unit_price: 2.0, price: 6.0, date: 11-04-2025, merchant: Your Company Inc.
+""")
+
+
+def ask_gemini(prompt: str) -> str:
+    response = client.models.generate_content(
+        model=MODEL_NAME,
+        contents=prompt,
+        config=types.GenerateContentConfig(
+            thinking_config=types.ThinkingConfig(thinking_budget=0)
+        ),
+    )
+    return response.text.strip()
+
+
+def generate_sql(question: str) -> str:
+    prompt = textwrap.dedent(f"""
+        You are a text-to-SQL generator on a food receipts database.
+
+        Here is the SQLite schema:
+        {SCHEMA}
+
+        Here are some example rows:
+        {EXAMPLES}
+
+        Generate a valid SQL query to answer this question:
+        {question}
+
+        Rules:
+        - Only return the SQL query.
+        - Do not include explanations or markdown formatting.
+    """)
+    sql = ask_gemini(prompt).strip("`")
+    if sql.lower().startswith("sql"):
+        sql = sql[3:].strip()
+    return sql
+
+
+def run_sql(question: str) -> str:
+    sql_query = generate_sql(question)
+    print(f"Generated SQL:\n{sql_query}")
+
+    try:
+        with sqlite3.connect(DB_NAME) as conn:
+            cur = conn.cursor()
+            cur.execute(sql_query)
+            results = cur.fetchall()
+    except Exception as e:
+        results = [("SQL Error", str(e))]
+
+    # Format into natural language
+    format_prompt = textwrap.dedent(f"""
+        You are a natural language formatter.
+
+        The user asked: {question}
+        The raw SQL result is: {results}
+
+        Write a concise, human-friendly answer.
+    """)
+    return ask_gemini(format_prompt)

receipt_ai_platform/app/ocr.py

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+import pytesseract
+from PIL import Image
+from dotenv import load_dotenv
+import re
+import os
+
+load_dotenv()
+
+tess_cmd = os.getenv("TESSERACT_CMD")
+if tess_cmd:
+    pytesseract.pytesseract.tesseract_cmd = tess_cmd
+
+
+def extract_receipt_text(image_path: str) -> str:
+    img = Image.open(image_path)
+    text = pytesseract.image_to_string(img)
+    print("Extracted Text:\n", text)
+    return text
+
+
+def parse_receipt(text: str) -> dict:
+    lines = text.split("\n")
+    items = []
+    receipt_date = None
+    merchant = None
+
+    date_match = re.search(r"Receipt date[:\s]+(\d{2}-\d{2}-\d{4})", text, re.IGNORECASE)
+    if date_match:
+        receipt_date = date_match.group(1)
+
+    for line in lines:
+        if re.search(r"(inc|company|store|shop)", line, re.IGNORECASE):
+            merchant = line.strip()
+            break
+
+    for line in lines:
+        match = re.match(r"(\d+)\s+(.+?)\s+([\d]+\.\d{2})\s+\$([\d]+\.\d{2})", line)
+        if match:
+            qty, item_name, unit_price, total_price = match.groups()
+
+            if any(keyword in item_name.lower() for keyword in ["subtotal", "tax", "total"]):
+                continue
+
+            items.append(
+                {
+                    "item": item_name.strip(),
+                    "quantity": int(qty),
+                    "unit_price": float(unit_price),
+                    "price": float(total_price),
+                }
+            )
+
+    return {
+        "items": items,
+        "date": receipt_date,
+        "merchant": merchant
+    }

receipt_ai_platform/requirements.txt

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+gradio==5.44.1
+pillow==11.3.0
+pytesseract==0.3.13
+python-dotenv==1.1.1
+google-genai==1.32.0