# ValueError: Image path is None/Invalid Imagedata dictionary

**URL:** <https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432>\
**Category:** Spaces\
**Created:** [January 13, 2025, 4:05am UTC](https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432 "2025-01-13T04:05:32Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![jays009](https://avatars.discourse-cdn.com/v4/letter/j/8c91f0/32.png) [@jays009](https://discuss.huggingface.co/u/jays009)\
**Post date:** [January 13, 2025, 4:05am UTC](https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432/1 "2025-01-13T04:05:32Z")

</div>

i tried loading my space as an API through gradio client and it went down smoothly but when i try calling the named api for prediction on an image which is stored locally on my system or be it an URL there’s an error on the space log as : pydantic\_core.\_pydantic\_core.ValidationError: 1 validation error for ImageData  
Input should be a valid dictionary or instance of ImageData [type=model\_type, input\_value=‘/9j/4AAQSkZJRgABAQAAAQAB…ULMJAMBsggdfzODQi4rU//Z’, input\_type=str]  
For further information visit [Redirecting...](https://errors.pydantic.dev/2.10/v/model_type)

i tried encoding it in base64 and i also updated my app.py script on how to process and return requests. but the issue persists. please any kind of help is appreciated.

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<div class="post-metadata">

**Author:** ![RajatChoudhary](https://avatars.discourse-cdn.com/v4/letter/r/8797f3/32.png) [@RajatChoudhary](https://discuss.huggingface.co/u/RajatChoudhary)\
**Post date:** [August 9, 2025, 8:39pm UTC](https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432/2 "2025-08-09T20:39:10Z")

</div>

bro got any solution ?? its 2 am now still struggling to resolve ☹

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [August 10, 2025, 12:05am UTC](https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432/3 "2025-08-10T00:05:17Z")

</div>

It seems that the documentation says to [use `handle_file`](https://www.gradio.app/guides/getting-started-with-the-python-client) (do not send `base64` directly)…

```py
from gradio_client import Client, handle_file

client = Client("user/space-name")
res = client.predict(image=handle_file("./cat.jpg"), api_name="/predict") # local file
res = client.predict(image=handle_file("https://example.com/cat.jpg"), api_name="/predict") # URL

```

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<div class="post-metadata">

**Author:** ![youssef-9511](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/youssef-9511/32/54830_2.png) [@youssef-9511](https://discuss.huggingface.co/u/youssef-9511)\
**Post date:** [October 14, 2025, 8:16pm UTC](https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432/4 "2025-10-14T20:16:59Z")

</div>

Does anyone have a solution to this ? I’m trying to integrate an API call in my android app through http for some image recognition, i don’t know how to pass in an local image. Only public URLs work.

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [October 14, 2025, 10:03pm UTC](https://discuss.huggingface.co/t/valueerror-image-path-is-none-invalid-imagedata-dictionary/135432/5 "2025-10-14T22:03:22Z")

</div>

Endpoints only accept data, not URLs or local paths. Therefore, if utilities or libraries for automatic conversion are unavailable, you must manually convert and pass the data…

* * *

Use the Hugging Face **Serverless Inference API** and send the **image bytes** (or base64) to the model URL with a Bearer token. Do not send a local file path. The API endpoint format is:

```auto
https://api-inference.huggingface.co/models/<MODEL_ID>

```

Example model id: `google/vit-base-patch16-224`. Docs verified 2025-10-15. ([Hugging Face](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api "Serverless Inference API - Hugging Face Open-Source AI Cookbook"))

# What the API expects

- Auth: `Authorization: Bearer hf_...` using a fine-grained user token. ([Hugging Face](https://huggingface.co/docs/hub/en/security-tokens "User access tokens"))

- Payloads for image tasks:

- Cold start: first call can return **503** while the model loads. Retry. ([Hugging Face](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api "Serverless Inference API - Hugging Face Open-Source AI Cookbook"))

# cURL quick tests

Binary bytes (recommended):

```bash
# Ref: https://huggingface.co/docs/inference-endpoints/supported_tasks (binary example also applies)
# https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api (endpoint format)
/usr/bin/curl -sS \
  -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: image/jpeg" \
  --data-binary "@/path/to/local.jpg" \
  "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"

```

JSON base64:

```bash
# Ref: https://huggingface.co/docs/inference-providers/en/tasks/image-classification
b64=$(base64 -w0 </path/to/local.jpg)
curl -sS -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{”inputs”:”$b64”}" \
  "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"

```

The Inference Providers spec explicitly allows base64 or raw bytes, and returns `{label, score}` objects. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/image-classification "Image Classification"))  
HF’s supported-tasks reference shows the exact `--data-binary` pattern for images. ([Hugging Face](https://huggingface.co/docs/inference-endpoints/supported_tasks "Supported Transformers & Diffusers Tasks - Hugging Face"))

# Android (OkHttp) — send local image bytes

```kotlin
// Ref:
// - API URL shape + 503 note: https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api
// - Payload options + response schema: https://huggingface.co/docs/inference-providers/en/tasks/image-classification
// - InferenceClient accepts bytes/paths/URLs for images: https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client

import android.content.Context
import android.net.Uri
import okhttp3.MediaType.Companion.toMediaType
import okhttp3.OkHttpClient
import okhttp3.Request
import okhttp3.RequestBody.Companion.toRequestBody

fun classifyLocalImage(
    context: Context,
    contentUri: Uri,
    hfToken: String,
    modelUrl: String = "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"
): String {
    val bytes = context.contentResolver.openInputStream(contentUri)!!.use { it.readBytes() }
    val body = bytes.toRequestBody("image/jpeg".toMediaType()) // set correct type if PNG/WebP
    val req = Request.Builder()
        .url(modelUrl)
        .addHeader("Authorization", "Bearer $hfToken")
        .addHeader("Content-Type", "image/jpeg")
        .post(body)
        .build()
    OkHttpClient().newCall(req).execute().use { resp ->
        if (!resp.isSuccessful) error("HTTP ${resp.code}: ${resp.body?.string()}")
        return resp.body!!.string() // e.g., [{"label":"tabby","score":0.99}, ...]
    }
}

```

Alternative: JSON base64 from Android

```kotlin
import android.util.Base64
import okhttp3.MediaType.Companion.toMediaType
import okhttp3.RequestBody.Companion.toRequestBody

fun classifyLocalImageAsJson(
    context: Context,
    contentUri: Uri,
    hfToken: String,
    modelUrl: String
): String {
    val bytes = context.contentResolver.openInputStream(contentUri)!!.use { it.readBytes() }
    val b64 = Base64.encodeToString(bytes, Base64.NO_WRAP) // avoid newlines
    val json = """{"inputs":"$b64"}"""
    val body = json.toRequestBody("application/json".toMediaType())
    val req = Request.Builder()
        .url(modelUrl)
        .addHeader("Authorization", "Bearer $hfToken")
        .post(body)
        .build()
    OkHttpClient().newCall(req).execute().use { resp ->
        if (!resp.isSuccessful) error("HTTP ${resp.code}: ${resp.body?.string()}")
        return resp.body!!.string()
    }
}

```

# Minimal checklist

1. Build the URL: `https://api-inference.huggingface.co/models/<MODEL_ID>`. ([Hugging Face](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api "Serverless Inference API - Hugging Face Open-Source AI Cookbook"))
2. Add `Authorization: Bearer <HF_TOKEN>`. Use a fine-grained token. ([Hugging Face](https://huggingface.co/docs/hub/en/security-tokens "User access tokens"))
3. Send **bytes** with `Content-Type: image/<ext>` or send **JSON** with `"inputs": "<base64>"`. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/image-classification "Image Classification"))
4. Handle `503` by retrying. Handle `429` by backing off. Rate limits for free users are a few hundred requests per hour; PRO increases limits. ([Hugging Face](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api "Serverless Inference API - Hugging Face Open-Source AI Cookbook"))
5. Expect image-classification output as `[{"label": "...","score": ...}, ...]`. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/image-classification "Image Classification"))

# Common mistakes

- Sending a **file path** string. The server cannot read client files. Send bytes or base64. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/image-classification "Image Classification"))
- Wrong `Content-Type`. Match your actual image type, or use `application/octet-stream`. ([Hugging Face](https://huggingface.co/docs/inference-endpoints/supported_tasks "Supported Transformers & Diffusers Tasks - Hugging Face"))
- Newlines in base64. Use no-wrap encoding on mobile to avoid bad JSON.
- Assuming JSON is required. Raw image bytes are accepted when you do not pass `parameters`. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/image-classification "Image Classification"))

# Short examples to copy

cURL, JPEG bytes:

```bash
# https://huggingface.co/docs/inference-endpoints/supported_tasks
curl -sS -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: image/jpeg" \
  --data-binary "@local.jpg" \
  "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"

```

cURL, JSON base64:

```bash
# https://huggingface.co/docs/inference-providers/en/tasks/image-classification
b64=$(base64 -w0 < local.jpg)
curl -sS -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{”inputs”:”$b64”}" \
  "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"

```

# Supplemental references

- **Serverless Inference API cookbook.** Endpoint format, auth, rate limits, cold-start behavior. Updated frequently. Checked 2025-10-15. ([Hugging Face](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api "Serverless Inference API - Hugging Face Open-Source AI Cookbook"))
- **Image Classification API spec.** Exact request shapes. Base64 vs raw bytes. Response schema. Checked 2025-10-15. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/image-classification "Image Classification"))
- **Supported tasks reference.** Shows concrete `--data-binary` image examples. Useful when crafting raw HTTP calls. Checked 2025-10-15. ([Hugging Face](https://huggingface.co/docs/inference-endpoints/supported_tasks "Supported Transformers & Diffusers Tasks - Hugging Face"))
- **User access tokens.** Bearer token usage and management. Checked 2025-10-15. ([Hugging Face](https://huggingface.co/docs/hub/en/security-tokens "User access tokens"))