UI shell

app.py

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+import gradio as gr
+import requests
+import os
+from PIL import Image
+import json
+from datetime import datetime
+
+
+# Example data with placeholder JSON for lab_results and bank_statement
+examples = [
+    ["bonds_table.png", "Bonds table", "[{\"instrument_name\":\"example\", \"valuation\":0}]"],
+    ["lab_results.png", "Lab results", "{\"patient_name\": \"example\", \"patient_age\": \"example\", \"patient_pid\": 0, \"lab_results\": [{\"investigation\": \"example\", \"result\": 0.00, \"reference_value\": \"example\", \"unit\": \"example\"}]}"],
+    ["bank_statement.png", "Bank statement", "*"]
+]
+
+# JSON data for Bonds table
+bonds_json = [
+    {
+        "instrument_name": "UNITS BLACKROCK FIX INC DUB FDS PLC ISHS EUR INV GRD CP BD IDX/INST/E",
+        "valuation": 19049
+    },
+    {
+        "instrument_name": "UNITS ISHARES III PLC CORE EUR GOVT BOND UCITS ETF/EUR",
+        "valuation": 83488
+    },
+    {
+        "instrument_name": "UNITS ISHARES III PLC EUR CORP BOND 1-5YR UCITS ETF/EUR",
+        "valuation": 213030
+    },
+    {
+        "instrument_name": "UNIT ISHARES VI PLC/JP MORGAN USD E BOND EUR HED UCITS ETF DIST/HDGD/",
+        "valuation": 32774
+    },
+    {
+        "instrument_name": "UNITS XTRACKERS II SICAV/EUR HY CORP BOND UCITS ETF/-1D-/DISTR.",
+        "valuation": 23643
+    }
+]
+
+lab_results_json = {
+    "patient_name": "Yash M. Patel",
+    "patient_age": "21 Years",
+    "patient_pid": 555,
+    "lab_results": [
+        {
+          "investigation": "Hemoglobin (Hb)",
+          "result": 12.5,
+          "reference_value": "13.0 - 17.0",
+          "unit": "g/dL"
+        },
+        {
+          "investigation": "RBC COUNT",
+          "result": 5.2,
+          "reference_value": "4.5 - 5.5",
+          "unit": "mill/cumm"
+        },
+        {
+          "investigation": "Packed Cell Volume (PCV)",
+          "result": 57.5,
+          "reference_value": "40 - 50",
+          "unit": "%"
+        },
+        {
+          "investigation": "Mean Corpuscular Volume (MCV)",
+          "result": 87.75,
+          "reference_value": "83 - 101",
+          "unit": "fL"
+        },
+        {
+          "investigation": "MCH",
+          "result": 27.2,
+          "reference_value": "27 - 32",
+          "unit": "pg"
+        },
+        {
+          "investigation": "MCHC",
+          "result": 32.8,
+          "reference_value": "32.5 - 34.5",
+          "unit": "g/dL"
+        },
+        {
+          "investigation": "RDW",
+          "result": 13.6,
+          "reference_value": "11.6 - 14.0",
+          "unit": "%"
+        },
+        {
+          "investigation": "WBC COUNT",
+          "result": 9000,
+          "reference_value": "4000-11000",
+          "unit": "cumm"
+        },
+        {
+          "investigation": "Neutrophils",
+          "result": 60,
+          "reference_value": "50 - 62",
+          "unit": "%"
+        },
+        {
+          "investigation": "Lymphocytes",
+          "result": 31,
+          "reference_value": "20 - 40",
+          "unit": "%"
+        },
+        {
+          "investigation": "Eosinophils",
+          "result": 1,
+          "reference_value": "00 - 06",
+          "unit": "%"
+        },
+        {
+          "investigation": "Monocytes",
+          "result": 7,
+          "reference_value": "00 - 10",
+          "unit": "%"
+        },
+        {
+          "investigation": "Basophils",
+          "result": 1,
+          "reference_value": "00 - 02",
+          "unit": "%"
+        },
+        {
+          "investigation": "Absolute Neutrophils",
+          "result": 6000,
+          "reference_value": "1500 - 7500",
+          "unit": "cells/mcL"
+        },
+        {
+          "investigation": "Absolute Lymphocytes",
+          "result": 3100,
+          "reference_value": "1300 - 3500",
+          "unit": "cells/mcL"
+        },
+        {
+          "investigation": "Absolute Eosinophils",
+          "result": 100,
+          "reference_value": "00 - 500",
+          "unit": "cells/mcL"
+        },
+        {
+          "investigation": "Absolute Monocytes",
+          "result": 700,
+          "reference_value": "200 - 950",
+          "unit": "cells/mcL"
+        },
+        {
+          "investigation": "Absolute Basophils",
+          "result": 100,
+          "reference_value": "00 - 300",
+          "unit": "cells/mcL"
+        },
+        {
+          "investigation": "Platelet Count",
+          "result": 320000,
+          "reference_value": "150000 - 410000",
+          "unit": "cumm"
+        }
+    ]
+}
+
+bank_statement_json = {
+  "bank": "First Platypus Bank",
+  "address": "1234 Kings St., New York, NY 12123",
+  "account_holder": "Mary G. Orta",
+  "account_number": "1234567890123",
+  "statement_date": "3/1/2022",
+  "period_covered": "2/1/2022 - 3/1/2022",
+  "account_summary": {
+    "balance_on_march_1": "$25,032.23",
+    "total_money_in": "$10,234.23",
+    "total_money_out": "$10,532.51"
+  },
+  "transactions": [
+    {
+      "date": "02/01",
+      "description": "PGD EasyPay Debit",
+      "withdrawal": "203.24",
+      "deposit": "",
+      "balance": "22,098.23"
+    },
+    {
+      "date": "02/02",
+      "description": "AB&B Online Payment*****",
+      "withdrawal": "71.23",
+      "deposit": "",
+      "balance": "22,027.00"
+    },
+    {
+      "date": "02/04",
+      "description": "Check No. 2345",
+      "withdrawal": "",
+      "deposit": "450.00",
+      "balance": "22,477.00"
+    },
+    {
+      "date": "02/05",
+      "description": "Payroll Direct Dep 23422342 Giants",
+      "withdrawal": "",
+      "deposit": "2,534.65",
+      "balance": "25,011.65"
+    },
+    {
+      "date": "02/06",
+      "description": "Signature POS Debit - TJP",
+      "withdrawal": "84.50",
+      "deposit": "",
+      "balance": "24,927.15"
+    },
+    {
+      "date": "02/07",
+      "description": "Check No. 234",
+      "withdrawal": "1,400.00",
+      "deposit": "",
+      "balance": "23,527.15"
+    },
+    {
+      "date": "02/08",
+      "description": "Check No. 342",
+      "withdrawal": "",
+      "deposit": "25.00",
+      "balance": "23,552.15"
+    },
+    {
+      "date": "02/09",
+      "description": "FPB AutoPay***** Credit Card",
+      "withdrawal": "456.02",
+      "deposit": "",
+      "balance": "23,096.13"
+    },
+    {
+      "date": "02/08",
+      "description": "Check No. 123",
+      "withdrawal": "",
+      "deposit": "25.00",
+      "balance": "23,552.15"
+    },
+    {
+      "date": "02/09",
+      "description": "FPB AutoPay***** Credit Card",
+      "withdrawal": "156.02",
+      "deposit": "",
+      "balance": "23,096.13"
+    },
+    {
+      "date": "02/08",
+      "description": "Cash Deposit",
+      "withdrawal": "",
+      "deposit": "25.00",
+      "balance": "23,552.15"
+    }
+  ]
+}
+
+
+def run_inference(image_filepath, query, key):
+    if image_filepath is None:
+        return {"error": f"No image provided. Please upload an image before submitting."}
+
+    if query is None or query.strip() == "":
+        return {"error": f"No query provided. Please enter a query before submitting."}
+
+    if key is None or key.strip() == "":
+        return {"error": f"No Sparrow Key provided. Please enter a Sparrow Key before submitting."}
+
+    file_path = None
+    try:
+        # Open the uploaded image using its filepath
+        img = Image.open(image_filepath)
+
+        # Extract the file extension from the uploaded file
+        input_image_extension = image_filepath.split('.')[-1].lower()  # Extract extension from filepath
+
+        # Set file extension based on the original file, otherwise default to PNG
+        if input_image_extension in ['jpg', 'jpeg', 'png']:
+            file_extension = input_image_extension
+        else:
+            file_extension = 'png'  # Default to PNG if extension is unavailable or invalid
+
+        # Generate a unique filename using timestamp
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"image_{timestamp}.{file_extension}"
+
+        # Save the image
+        img.save(filename)
+
+        # Get the full path of the saved image
+        file_path = os.path.abspath(filename)
+
+        # Prepare the REST API call
+        url = 'https://katanaml-sparrow-ml.hf.space/api/v1/sparrow-llm/inference'
+        headers = {
+            'accept': 'application/json'
+        }
+
+        # Open the file in binary mode and send it
+        with open(filename, "rb") as f:
+            files = {
+                'file': (filename, f, f'image/{file_extension}')
+            }
+
+            # Convert 'query' input to JSON string if needed
+            try:
+                # Check if the query is a wildcard '*'
+                if query.strip() == "*":
+                    query_json = "*"  # Directly use the wildcard as valid input
+                else:
+                    # Attempt to parse the query as JSON
+                    query_json = json.loads(query)  # This could return any valid JSON (string, number, etc.)
+
+                    # Ensure the parsed query is either a JSON object (dict) or a list of JSON objects
+                    if not isinstance(query_json, (dict, list)):
+                        return {
+                            "error": "Invalid input. Only JSON objects, arrays of objects, or wildcard '*' are allowed."}
+
+                    # If it's a list, make sure it's a list of JSON objects
+                    if isinstance(query_json, list):
+                        if not all(isinstance(item, dict) for item in query_json):
+                            return {"error": "Invalid input. Arrays must contain only JSON objects."}
+
+            except json.JSONDecodeError:
+                return {"error": "Invalid JSON format in query input"}
+
+            data = {
+                'group_by_rows': '',
+                'agent': 'sparrow-parse',
+                'keywords': '',
+                'sparrow_key': key,
+                'update_targets': '',
+                'debug': 'false',
+                'index_name': '',
+                'types': '',
+                'fields': query_json if query_json == "*" else json.dumps(query_json),  # Use wildcard as-is, or JSON
+                'options': 'huggingface,katanaml/sparrow-qwen2-vl-7b'
+            }
+
+            # Perform the POST request
+            response = requests.post(url, headers=headers, files=files, data=data)
+
+            # Process the response and return the JSON data
+            if response.status_code == 200:
+                return response.json()
+            else:
+                return {"error": f"Request failed with status code {response.status_code}", "details": response.text}
+    finally:
+        # Clean up the temporary file
+        if os.path.exists(file_path):
+            os.remove(file_path)
+
+
+def handle_example(example_image):
+    # Find the corresponding entry in the examples array
+    for example in examples:
+        if example[0] == example_image:
+            # Return bonds_json if Bonds table is selected
+            if example_image == "bonds_table.png":
+                return example_image, bonds_json, example[2]
+            # Return lab_results_json if Lab results is selected
+            elif example_image == "lab_results.png":
+                return example_image, lab_results_json, example[2]
+            # Return bank_statement_json if Bank statement is selected
+            elif example_image == "bank_statement.png":
+                return example_image, bank_statement_json, example[2]
+
+    # Default return if no match found
+    return None, "No example selected.", ""
+
+
+# Define the UI
+with gr.Blocks(theme=gr.themes.Ocean()) as demo:
+    with gr.Tab(label="Sparrow UI"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Document Image", type="filepath")
+                query_input = gr.Textbox(label="Query", placeholder="Use * to query all data or JSON schema, e.g.: [{\"instrument_name\": \"example\"}]")
+                key_input = gr.Textbox(label="Sparrow Key", type="password")
+                submit_btn = gr.Button(value="Submit", variant="primary")
+
+                # Radio button for selecting examples
+                example_radio = gr.Radio(label="Select Example", choices=[ex[0] for ex in examples])
+
+            with gr.Column():
+                # JSON output for structured JSON display
+                output_json = gr.JSON(label="Response (JSON)", height=900, min_height=900)
+
+
+        # Function to handle example selection
+        def on_example_select(selected_example):
+            # Handle example selection and return the image, output (text or JSON), and query
+            return handle_example(selected_example)
+
+
+        # Update image, output JSON, and query when an example is selected
+        example_radio.change(on_example_select,
+                             inputs=example_radio,
+                             outputs=[input_img, output_json, query_input])
+
+        # When submit is clicked
+        submit_btn.click(run_inference, [input_img, query_input, key_input], [output_json])
+
+        gr.Markdown(
+            """
+            ---
+            <p style="text-align: center;">
+            Visit <a href="https://katanaml.io/" target="_blank">Katana ML</a> for more details.
+            </p>
+            """
+        )
+
+# Launch the app
+demo.queue(api_open=False)
+demo.launch(debug=True)

requirements.txt

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+gradio==5.1.0