README.md

metadata

title: 📊 Multimodal Financial Forecast
emoji: 📈
colorFrom: indigo
colorTo: green
sdk: docker
pinned: false

📊 Multimodal Financial Forecast

This app predicts future financial values from time series using an LSTM model. Input sequences or upload CSVs to get started.

🔧 Tech Stack

• Backend: FastAPI (app/main.py)
• ML Model: PyTorch LSTM
• Deployment: Hugging Face Spaces (Docker)

🚀 How to Use

1. Input a JSON-like sequence.
2. Or upload a .csv file with numerical sequences.
3. Click “Predict” to see the model forecast.

🧠 How It Works

1. User Input:

   • You can enter sequences directly or upload a .csv with your own data.

2. Model Inference:

   • The LSTM model is loaded from pipeline/trained_models/.
   • Input sequences are normalized and passed into the model.
   • The output is a forecast of future values.

3. Output:

   • The results are displayed directly on the webpage.
   • Graph removed (clean, console-style output).

---

🛠 Tech Stack

Layer	Tech
Backend	FastAPI
ML Model	PyTorch LSTM
Frontend	HTML + CSS
Deployment	Docker + Hugging Face Spaces

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📂 Project Structure

multimodal-financial-forecast/ ├── app/ │ ├── main.py # FastAPI backend │ ├── templates/ │ │ └── index.html # User interface │ └── static/ │ └── style.css # Stylesheet │ ├── pipeline/ │ ├── inference_pipeline.py # Model loading and inference │ └── trained_models/ │ ├── lstm_forecaster.pt # Trained LSTM model │ └── config.json # Input/output config │ ├── requirements.txt # Python dependencies ├── Dockerfile # Docker config ├── README.md # Project overview └── .gitignore

✅ Requirements

Install dependencies locally (for testing):

pip install -r requirements.txt

## Run the app locally:

uvicorn app.main:app --reload

## 📁 Example CSV Format

Sample input file (for upload):

0.1,0.2,0.3,0.4,0.5
0.5,0.6,0.7,0.8,0.9


## 🐳 Docker Deployment

FROM python:3.10-slim

WORKDIR /app

COPY . .

RUN pip install --upgrade pip && pip install -r requirements.txt

EXPOSE 7860

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]


## Build and Run Locally

docker build -t multimodal-forecast .
docker run -p 7860:7860 multimodal-forecast