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CV2Net.py

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+# define analysis engine
+from google.genai.types import Tool, GenerateContentConfig, GoogleSearch
+from google.genai import types
+from google import genai
+from io import BytesIO
+from PIL import Image
+import pandas as pd
+import gradio as gr
+import base64
+import json
+import os
+
+
+def cv2net(image_path,api_key):
+  # authenticate gemini client
+  client = genai.Client(api_key=api_key)
+
+  # call Google Search tool
+  google_search_tool = Tool(
+      google_search = GoogleSearch()
+  )
+
+  with open(image_path, 'rb') as f:
+    image_data = f.read()
+
+  prompt = """
+  I want you to carefully analyze the image(s) and map the functional relationship between every single identified entity in the image.
+  Do not ignore small or partially visible items. Collect the following information from the image(s) and DO NOT include items, objects, or things that are not in the image(s):
+  - Specific object name or person
+  - Precise functional relationship verb
+  - Class: object, person, animal, environment, text, brand
+  - Primary function or role
+  - Dominant color
+  - Small, medium, large, tiny, huge
+  - Material type
+  - Location description
+  - Current condition
+  - Spatial context
+  - Setting or environment
+  - Relationship strength: strong, medium, weak
+  - Spatial context
+  - Scene context
+  - Confidence: high, medium, low
+  - Today's date (YYYY-MM-DD)
+  Ignore what a person in an image is wearing. Return the results as one JSON file with the following structure exactly:
+  ```json
+    [
+      {
+        "Vertex1": "specific_object_name_or_person",
+        "Vertex2": "specific_object_name_or_person",
+        "Relationship": "precise_functional_relationship_verb",
+        "Vertex1_class": "Object|Person|Animal|Environment|Text|Brand",
+        "Vertex1_purpose": "primary_function_or_role",
+        "Vertex1_size": "small|medium|large|tiny|huge",
+        "Vertex1_position": "location_description",
+        "Vertex1_state": "current_condition",
+        "Vertex2_class": "Object|Person|Animal|Environment|Text|Brand",
+        "Vertex2_purpose": "primary_function_or_role",
+        "Vertex2_size": "small|medium|large|tiny|huge",
+        "Vertex2_position": "location_description",
+        "Vertex2_state": "current_condition",
+        "Relationship_type": "spatial|functional|contextual|interactive",
+        "Relationship_strength": "strong|medium|weak",
+        "Spatial_context": "detailed_spatial_description",
+        "Scene_context": "setting_or_environment",
+        "Confidence": "high|medium|low",
+        "Date": "today's_date"
+      }
+    ]
+    ```
+Here is an example JSON output:
+```json
+    [
+      {
+        "Vertex1": "Man",
+        "Vertex2": "Bench",
+        "Relationship": "Sits on",
+        "Vertex1_class": "Person",
+        "Vertex1_purpose": "Posing for photo",
+        "Vertex1_size": "Medium",
+        "Vertex1_position": "Left foreground",
+        "Vertex1_state": "Visible",
+        "Vertex2_class": "Object",
+        "Vertex2_purpose": "A seat",
+        "Vertex2_size": "Medium",
+        "Vertex2_position": "Middle ground",
+        "Vertex2_state": "Visible",
+        "Relationship_type": "Functional",
+        "Relationship_strength": "Strong",
+        "Spatial_context": "Man is sitting on bench",
+        "Scene_context": "Outdoor scene in the park",
+        "Confidence": "High",
+        "Date": "2025-07-16"
+      }
+    ]
+    ```
+"""
+
+  response = client.models.generate_content(
+      model="gemini-2.0-flash",
+      contents=[types.Part.from_bytes(data=image_data, mime_type="image/jpeg"), prompt],
+      config=GenerateContentConfig(
+          tools=[google_search_tool],
+          response_modalities=["TEXT"],
+          response_mime_type="application/json",
+      )
+  )
+
+  try:
+    # convert response from string to JSON
+    json_file = json.loads(response.text)
+
+    # convert JSON into a DataFrame
+    df = pd.DataFrame(json_file)
+    return df
+  except json.JSONDecodeError as e:
+    print(f"Error decoding JSON for image: {image_data} - {e}")
+    return None

Download.py

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+import pandas as pd
+
+def save_csv(df):
+    if df is None or len(df) == 0:
+        return None
+    file_path = "data.csv"
+    df.to_csv(file_path, index=False)
+    return file_path

ImageNet.py

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+from CV2Net import cv2net
+import gradio as gr
+import pandas as pd
+
+def image2net(img_file,api_key):
+  try:
+    if isinstance(img_file, list) and len(img_file) > 1:
+      df_list = []
+      for i in img_file:
+        df_list.append(cv2net(i,api_key))
+
+    else:
+      df_list = [cv2net(img_file,api_key)]
+
+  except Exception:
+      gr.Info("The model is overloaded. Please try again later!")
+
+  # Filter out None values before concatenating
+  valid_dfs = [df for df in df_list if df is not None]
+
+  if valid_dfs:
+    df = pd.concat(valid_dfs)
+    file_path = "network_data.csv"
+    df.to_csv(file_path, index=False)
+    return df, file_path
+  else:
+    return None, None