This application uses a deep learning model to generate a description of an uploaded image. Users can also provide feedback to fine-tune the model.

To set up and run this application, follow these steps:

1. Create and activate a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

2. Install the required dependencies:

   pip install -r requirements.txt

To run the application, use the following command:

streamlit run app.py

• Python: Version 3.8 or higher.
• Memory (RAM): At least 8 GB of RAM is recommended, as the model can be memory-intensive.
• Storage: At least 2 GB of free disk space is required for the model and its dependencies.
• Internet Connection: An internet connection is required to download the pre-trained model on the first run.

Before you begin, please be aware of the following:

• Model Size: The fine-tuned model is approximately 800 MB. Please be patient when cloning the repository or downloading the model for the first time.
• Model Accuracy: This model was fine-tuned on a very limited dataset consisting only of raccoon images. As a result, its accuracy is quite low, and it will likely fail to generate correct descriptions for most images. The feedback.csv file highlights these inaccuracies. The primary purpose of this project is to demonstrate the architecture and the feedback loop, not to provide a production-ready service.
• Resource Consumption: Running this model is resource-intensive. It requires a significant amount of system memory (RAM) to load and perform inference. Ensure your machine has sufficient resources available before running the application.

The project follows a client-server architecture where a web-based frontend communicates with a Python backend that hosts the machine learning model.

graph TD
    subgraph Client
        A[User Browser]
    end

    subgraph Server
        B[Python Web Server e.g., Flask, FastAPI]
        C[Fine-Tuned BLIP Model]
        D[feedback.csv]
    end

    A -- 1. Upload Image --> B
    B -- 2. Preprocess Image --> C
    C -- 3. Generate Description --> B
    B -- 4. Send Description --> A
    A -- 5. Submit Feedback/Correction --> B
    B -- 6. Log Feedback --> D

• Client (User Browser): A simple web interface that allows a user to upload an image file and view the model-generated description. It also provides a way for the user to submit a corrected description.
• Python Web Server: A backend server responsible for handling HTTP requests from the client. It manages image data, interacts with the ML model, and logs user feedback.
• Fine-Tuned BLIP Model: The core of the project. This is a BlipForConditionalGeneration model, as specified in the fine-tuned-model/config.json. It takes an image as input and generates a text description.
• feedback.csv: A simple CSV file that acts as a database to store the original image, the model's generated description, and the user's feedback. This data is invaluable for analyzing model performance and for future retraining cycles.

The workflow is a straightforward process involving image upload, model inference, and a feedback loop.

1. Image Upload: The process begins when a user selects an image from their local machine and uploads it through the web frontend. The browser sends the image data to the backend server.

2. Inference Request: The Python backend receives the image. It preprocesses the image to match the input requirements of the vision model (e.g., resizing to 384x384 pixels, as defined in vision_config).

3. Description Generation: The preprocessed image tensor is passed to the fine-tuned BLIP model. The model, which combines a vision transformer and a text decoder, performs inference to generate a descriptive caption for the image.

4. Displaying the Result: The backend server sends the generated text description back to the client's browser, where it is displayed to the user.

5. User Feedback: The user reviews the generated description. If it's inaccurate or incomplete, they can type a correction into a feedback form. The feedback.csv file shows examples of this, such as correcting "racco" to "It is a raccoon."

6. Logging for Improvement: When the user submits the feedback, the frontend sends the corrected text to the backend. The backend then appends a new row to feedback.csv, recording the image identifier, the model's original (and incorrect) output, and the user's superior description. This creates a valuable dataset that can be used to further fine-tune the model and improve its accuracy over time.

The feedback.csv file contains an entry with an ImportError: cannot import name 'AdamW' from 'transformers'. This suggests a runtime issue in the backend, likely related to an outdated version of the transformers library or a dependency mismatch. AdamW was moved from the top-level transformers package to transformers.optimization.

To fix this, you should update the import statement in your training or inference script.

Example Code Fix:

--- a/your_script.py
+++ b/your_script.py
@@ -1,4 +1,4 @@
- from transformers import AdamW
+ from transformers.optimization import AdamW
  # ... other imports

Ensuring your environment is correctly configured is crucial for the model to run as expected.