Upload folder using huggingface_hub

README.md

@@ -1,211 +1,19 @@
-<!-- Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -->
-
-<picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://assets.vespa.ai/logos/Vespa-logo-green-RGB.svg">
-  <source media="(prefers-color-scheme: light)" srcset="https://assets.vespa.ai/logos/Vespa-logo-dark-RGB.svg">
-  <img alt="#Vespa" width="200" src="https://assets.vespa.ai/logos/Vespa-logo-dark-RGB.svg" style="margin-bottom: 25px;">
-</picture>
-
-# 🔍 Visual Retrieval ColPali 👀
-
-This readme contains the code for a web app including a frontend that can be set up as a user facing interface.
-
-## Why?
-
-To enable _you_ to showcase the power of ColPali and Vespa to your users, and to provide a starting point for your own projects.
-
-> "But I only know Python, how can I create a web app that's not Gradio or Streamlit?" 🤔
-
-No worries! This project uses [FastHTML](https://fastht.ml/) to create a beautiful web app - and it's all Python! 🐍
-
-Also, 👇
-
-<a href="https://imgflip.com/i/98mhch"><img src="https://i.imgflip.com/98mhch.jpg" title="made at imgflip.com" alt="Funny meme about json output in demo"/></a>
-
-As a prerequisite, you should run [this notebook](https://pyvespa.readthedocs.io/en/latest/examples/visual_pdf_rag_with_vespa_colpali_cloud.ipynb) to prepare the data and deploy the Vespa application.
-
-## Setting up your .env variables
-
-The following variables are required in your `.env` file for the application to be able to connect to the Vespa application and the Gemini API:
-
-You can rename the `.env.example` file to `.env` and fill in the required values.
-The other variables are optional, if you want to use mTLS authentication against the Vespa application.
-
-```bash
-VESPA_APP_TOKEN_URL=https://abcde.z.vespa-app.cloud
-VESPA_CLOUD_SECRET_TOKEN=vespa_cloud_xxxxxxxx
-GEMINI_API_KEY=asdf
-```
-
-If you want to deploy the application to Huggingface, you also need to set a `HF_TOKEN` variable, with write permissions.
-This is personal, and must be created at [huggingface](https://huggingface.co/settings/tokens).
-
-```bash
-HF_TOKEN=hf_xxxxxxxxxx
-```
-
-## Setting up python environment
-
-This application should work on Python 3.8 and above.
-
-You can install the dependencies with `pip`, but we recommend using `uv`. 
-Skip to [Installing dependencies using `uv`](#installing-dependencies-using-uv) if you want to use `uv`.
-
-### Installing dependencies using `pip`
-
-You can install the dependencies with `pip`:
-
-```bash
-pip install -r src/requirements.txt
-```
-
-### Installing dependencies using `uv`
-
-We recommend installing the amazing `uv` to manage your python environment:
-See also [installation - uv docs](https://docs.astral.sh/uv/getting-started/installation/) for other installation options.
-
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-```
-
-Then, create a virtual environment:
-
-```bash
-uv venv 
-```
-
-Activate the virtual environment:
-
-```bash
-source .venv/bin/activate
-```
-
-Sync your virtual environment with the dependencies:
-
-```bash
-uv sync --extra dev
-```
-
-## Running the application locally
-
-To run the application locally, you can change into the `src` directory and run:
-
-```bash
-python main.py
-```
-
-This will start a local server, and you can access the application at `http://localhost:7860`.
-
-## Deploy to huggingface 🤗 spaces
-
-### Compiling dependencies
-
-Before a deploy, make sure to run this to compile the `uv` lock file to `requirements.txt` if you have made changes to the dependencies:
-
-```bash
-uv pip compile pyproject.toml -o src/requirements.txt
-```
-
-This will make sure that the dependencies in your `pyproject.toml` are compiled to the `requirements.txt` file, which is used by the huggingface space.
-
-### Deploying to huggingface
-
-Note that you need to set `HF_TOKEN` environment variable first.
-This is personal, and must be created at [huggingface](https://huggingface.co/settings/tokens).
-Make sure the token has `write` access.
-Be aware that this will not delete existing files, only modify or add,
-see [huggingface-cli](https://huggingface.co/docs/huggingface_hub/en/guides/upload#upload-from-the-cli) for more
-information.
-
-#### Update your space configuration
-
-The `src/README.md` file contains the configuration for the space.
-Feel free to update this file to match your own configuration - name, description, etc.
-
-Note that we can actually use the `gradio` SDK of spaces, to serve FastHTML apps as well, as long as we serve the app on port `7860`.
-See [Custom python spaces](https://huggingface.co/docs/hub/en/spaces-sdks-python) for more information.
-
-#### Upload the files
-
-To deploy, run
-
-(Replace `vespa-engine/colpali-vespa-visual-retrieval` with your own huggingface user/repo name, does not need to exist beforehand)
-
-```bash
-huggingface-cli upload vespa-engine/colpali-vespa-visual-retrieval src . --repo-type=space
-```
-
-Note that we upload only the `src` directory.
-
-## Development
-
-This section applies if you want to make changes to the web app.
-
-### Adding dependencies
-
-To add dependencies, you can add them to the `pyproject.toml` file and run:
-
-```bash
-uv compile
-```
-
-and then sync the dependencies:
-
-```bash
-uv sync --extra dev
-```
-
-### Making changes to CSS
-
-To make changes to output.css apply, run
-
-```bash
-shad4fast watch # watches all files passed through the tailwind.config.js content section
-
-shad4fast build # minifies the current output.css file to reduce bundle size in production.
-```
-
-### Instructions on creating and feeding the full dataset
-
-This section is only relevant if you want to create and feed the full dataset to Vespa.
-The notebook referenced in the beginning of this readme should be sufficient if you just want to spin up a scaled down version of the demo.
-
-#### Prepare data and Vespa application
-
-First, install `uv`:
-
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-```
-
-Then, run:
-
-```bash
-uv sync --extra dev --extra feed
-```
-
-#### Converting to notebook
-
-If you want to run the `prepare_feed_deploy.py` as a notebook, you can convert it using `jupytext`:
-
-Convert the `prepare_feed_deploy.py` to notebook to:
-
-```bash
-jupytext --to notebook prepare_feed_deploy.py
-```
-
-And launch a Jupyter instance with:
-
-```bash
-uv run --with jupyter jupyter lab
-```
-
-## Credits
-
-Huge thanks to the amazing projects that made it a joy to create this demo 🙏🙌
-
-- Freeing us from python dependency hell - [uv](https://astral.sh/uv/)
-- Allowing us to build **beautiful** full stack web apps in Python [FastHTML](https://fastht.ml/)
-- Introducing the ColPali architecture - [ColPali](https://huggingface.co/vidore/colpali-v1.2)
-- Adding `shadcn` components to FastHTML - [Shad4Fast](https://www.shad4fasthtml.com/)
-  
+---
+title: ColPali 🤝 Vespa - Visual Retrieval
+short_description: Visual Retrieval with ColPali and Vespa
+emoji: 👀
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: main.py
+pinned: false
+license: apache-2.0
+suggested_hardware: t4-small
+models:
+  - vidore/colpaligemma-3b-pt-448-base
+  - vidore/colpali-v1.2
+preload_from_hub:
+  - vidore/colpaligemma-3b-pt-448-base config.json,model-00001-of-00002.safetensors,model-00002-of-00002.safetensors,model.safetensors.index.json,preprocessor_config.json,special_tokens_map.json,tokenizer.json,tokenizer_config.json 12c59eb7e23bc4c26876f7be7c17760d5d3a1ffa
+  - vidore/colpali-v1.2 adapter_config.json,adapter_model.safetensors,preprocessor_config.json,special_tokens_map.json,tokenizer.json,tokenizer_config.json 9912ce6f8a462d8cf2269f5606eabbd2784e764f
+---

backend/vespa_app.py

@@ -376,10 +376,11 @@ class VespaQueryClient:
     async def get_suggestions(self, query: str) -> list:
         async with self.app.asyncio(connections=1) as session:
             start = time.perf_counter()
-            yql = f'select questions from {self.VESPA_SCHEMA_NAME} where questions matches "{query}" limit 3'
+            yql = f'select questions from {self.VESPA_SCHEMA_NAME} where questions matches (".*{query}.*")'
             response: VespaQueryResponse = await session.query(
                 body={
                     "yql": yql,
+                    "query": query,
                     "ranking": "unranked",
                     "presentation.timing": True,
                     "presentation.summary": "suggestions",
@@ -401,8 +402,15 @@ class VespaQueryClient:
                 for result in search_results
                 if "questions" in result["fields"]
             ]
-            flat_questions = [item for sublist in questions for item in sublist]
-            return flat_questions
+            for q in questions:
+                print(q)
+            unique_questions = set([item for sublist in questions for item in sublist])
+
+            # remove an artifact from our data generation
+            if "string" in unique_questions:
+                unique_questions.remove("string")
+
+            return list(unique_questions)
 
     def get_rank_profile(self, ranking: str, sim_map: bool) -> str:
         if sim_map:

main.py

@@ -220,7 +220,8 @@ async def get(session, request, query: str, ranking: str):
         f"Search results fetched in {end - start:.2f} seconds. Vespa search time: {result['timing']['searchtime']}"
     )
     search_time = result["timing"]["searchtime"]
-    total_count = result["root"]["fields"]["totalCount"]
+    # Safely get total_count with a default of 0
+    total_count = result.get("root", {}).get("fields", {}).get("totalCount", 0)
 
     search_results = vespa_app.results_to_search_results(result, idx_to_token)