Initial commit of ML Model Hub

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy the dependencies file to the working directory
+COPY ./requirements.txt /code/requirements.txt
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the rest of the application's code to the working directory
+COPY . /code/
+
+# Expose the port the app runs on
+EXPOSE 7860
+
+# Define the command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: Ml Model Hub
-emoji: 🌍
-colorFrom: indigo
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+import logging
+import io
+from pathlib import Path
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import tensorflow as tf
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# --- Configuration ---
+logging.basicConfig(level=logging.INFO)
+STATIC_DIR = Path("static")
+
+# --- Device Configuration ---
+device = torch.device('cpu')
+try:
+    tf.config.set_visible_devices([], 'GPU')
+    logging.info("TensorFlow GPU disabled. Using CPU.")
+except (RuntimeError, ValueError) as e:
+    logging.warning(f"Could not disable GPU for TensorFlow: {e}")
+
+# --- Model Loading ---
+def load_models():
+    """Load all models from Hugging Face Hub at startup."""
+    logging.info("Loading all models from the Hub...")
+    try:
+        tokenizer = AutoTokenizer.from_pretrained("muhalwan/sental")
+        sentiment_model = AutoModelForSequenceClassification.from_pretrained("muhalwan/sental")
+        sentiment_model.to(device)
+        logging.info("Sentiment analysis model loaded successfully.")
+    except Exception as e:
+        tokenizer, sentiment_model = None, None
+        logging.error(f"Error loading sentiment model: {e}")
+
+    try:
+        model_path = hf_hub_download(repo_id="muhalwan/catndog", filename="catdog_best.keras")
+        cat_dog_model = tf.keras.models.load_model(model_path, compile=False)
+        logging.info("Cat & Dog classifier model loaded successfully.")
+    except Exception as e:
+        cat_dog_model = None
+        logging.error(f"Error loading cat & dog model: {e}")
+
+    return tokenizer, sentiment_model, cat_dog_model
+
+# --- FastAPI App Initialization ---
+app = FastAPI()
+tokenizer, sentiment_model, cat_dog_model = load_models()
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+class SentimentRequest(BaseModel):
+    text: str
+
+# --- API Endpoints ---
+@app.get("/")
+async def read_root():
+    return FileResponse('index.html')
+
+@app.post("/predict/sentiment")
+async def predict_sentiment(request: SentimentRequest):
+    if not tokenizer or not sentiment_model:
+        raise HTTPException(status_code=503, detail="Sentiment model is not available.")
+    try:
+        inputs = tokenizer(request.text, return_tensors='pt', truncation=True, max_length=512)
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        with torch.no_grad():
+            outputs = sentiment_model(**inputs)
+        probabilities = F.softmax(outputs.logits, dim=-1).squeeze()
+        labels = ['Bearish', 'Bullish', 'Neutral']
+        prediction = labels[torch.argmax(probabilities).item()]
+        return {"prediction": prediction}
+    except Exception as e:
+        logging.error(f"Sentiment prediction error: {e}")
+        raise HTTPException(status_code=500, detail="An error occurred during sentiment analysis.")
+
+@app.post("/predict/catdog")
+async def predict_catdog(file: UploadFile = File(...)):
+    if not cat_dog_model:
+        raise HTTPException(status_code=503, detail="Cat & Dog model is not available.")
+    try:
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+        _, height, width, _ = cat_dog_model.input_shape
+        img_resized = image.resize((width, height))
+        if img_resized.mode == 'RGBA':
+            img_resized = img_resized.convert('RGB')
+        img_array = tf.keras.utils.img_to_array(img_resized)
+        img_array = tf.keras.applications.efficientnet.preprocess_input(img_array)
+        img_array = np.expand_dims(img_array, axis=0)
+        prob = cat_dog_model.predict(img_array, verbose=0)[0, 0]
+        label = "Dog" if prob >= 0.5 else "Cat"
+        return {"prediction": label}
+    except Exception as e:
+        logging.error(f"Cat/Dog prediction error: {e}")
+        raise HTTPException(status_code=500, detail="An error occurred during image classification.")

index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>ML Model Hub</title>
+    <link rel="stylesheet" href="/static/style.css">
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>ML Model Hub</h1>
+            <p>Select a model to get started</p>
+        </header>
+
+        <div class="model-selector">
+            <button id="cat-dog-btn" class="model-btn">Cat & Dog Classifier</button>
+            <button id="sentiment-btn" class="model-btn active">Sentiment Analysis</button>
+            <button class="model-btn disabled" disabled>California House Price (soon)</button>
+        </div>
+
+        <main id="main-content">
+            <!-- Sentiment Analysis Interface -->
+            <div id="sentiment-interface" class="model-interface active">
+                <form id="sentiment-form">
+                    <h2>Tweet Sentiment Analysis</h2>
+                    <p>Enter a tweet to analyze its sentiment (Bullish, Bearish, or Neutral).</p>
+                    <label for="tweet-text">Tweet Text</label>
+                    <textarea id="tweet-text" placeholder="Enter tweet here..."></textarea>
+                    <button type="submit" class="predict-btn">Predict</button>
+                </form>
+                <div id="sentiment-result" class="result-display"></div>
+            </div>
+
+            <!-- Cat & Dog Classifier Interface -->
+            <div id="cat-dog-interface" class="model-interface">
+                <form id="cat-dog-form">
+                    <h2>Cat & Dog Classifier</h2>
+                    <p>Upload an image to classify if it's a cat or a dog.</p>
+                    <label for="image-upload" class="upload-label">Choose an image...</label>
+                    <input type="file" id="image-upload" accept="image/jpeg, image/png">
+                    <div id="image-preview"></div>
+                    <button type="submit" class="predict-btn">Predict</button>
+                </form>
+                <div id="cat-dog-result" class="result-display"></div>
+            </div>
+        </main>
+    </div>
+
+    <script src="/static/script.js"></script>
+</body>
+</html>

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cpu
+numpy==1.26.4
+pandas==2.0.3
+torch
+transformers==4.50.0
+sentencepiece
+huggingface-hub==0.26
+tensorflow
+Pillow
+types-pytz
+fastapi
+uvicorn[standard]
+python-multipart

static/script.js

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+document.addEventListener('DOMContentLoaded', () => {
+    const sentimentBtn = document.getElementById('sentiment-btn');
+    const catDogBtn = document.getElementById('cat-dog-btn');
+    const sentimentInterface = document.getElementById('sentiment-interface');
+    const catDogInterface = document.getElementById('cat-dog-interface');
+
+    const sentimentForm = document.getElementById('sentiment-form');
+    const catDogForm = document.getElementById('cat-dog-form');
+    const tweetText = document.getElementById('tweet-text');
+    const imageUpload = document.getElementById('image-upload');
+    const imagePreview = document.getElementById('image-preview');
+
+    const sentimentResult = document.getElementById('sentiment-result');
+    const catDogResult = document.getElementById('cat-dog-result');
+
+    function switchModelView(activeBtn, activeInterface) {
+        [sentimentBtn, catDogBtn].forEach(btn => btn.classList.remove('active'));
+        activeBtn.classList.add('active');
+
+        [sentimentInterface, catDogInterface].forEach(intf => intf.classList.remove('active'));
+        activeInterface.classList.add('active');
+    }
+
+    sentimentBtn.addEventListener('click', () => switchModelView(sentimentBtn, sentimentInterface));
+    catDogBtn.addEventListener('click', () => switchModelView(catDogBtn, catDogInterface));
+
+    imageUpload.addEventListener('change', () => {
+        const file = imageUpload.files[0];
+        if (file) {
+            const reader = new FileReader();
+            reader.onload = (e) => {
+                imagePreview.innerHTML = `<img src="${e.target.result}" alt="Image preview">`;
+            };
+            reader.readAsDataURL(file);
+        }
+    });
+
+    sentimentForm.addEventListener('submit', async (e) => {
+        e.preventDefault();
+        const text = tweetText.value.trim();
+        if (!text) return;
+
+        showResult(sentimentResult, 'Analyzing...', 'loading');
+
+        try {
+            const response = await fetch('/predict/sentiment', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({ text })
+            });
+            const data = await response.json();
+            if (!response.ok) throw new Error(data.detail || 'Prediction failed');
+
+            showResult(sentimentResult, `Prediction: ${data.prediction}`, 'success');
+        } catch (error) {
+            showResult(sentimentResult, `Error: ${error.message}`, 'error');
+        }
+    });
+
+    catDogForm.addEventListener('submit', async (e) => {
+        e.preventDefault();
+        const file = imageUpload.files[0];
+        if (!file) return;
+
+        showResult(catDogResult, 'Classifying...', 'loading');
+
+        const formData = new FormData();
+        formData.append('file', file);
+
+        try {
+            const response = await fetch('/predict/catdog', {
+                method: 'POST',
+                body: formData
+            });
+            const data = await response.json();
+            if (!response.ok) throw new Error(data.detail || 'Classification failed');
+
+            showResult(catDogResult, `Prediction: ${data.prediction}`, 'success');
+        } catch (error) {
+            showResult(catDogResult, `Error: ${error.message}`, 'error');
+        }
+    });
+
+    function showResult(element, message, type) {
+        element.innerHTML = message;
+        element.className = 'result-display';
+        element.classList.add(type);
+        element.style.display = 'block';
+    }
+});

static/style.css

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+body {
+    font-family: 'Inter', sans-serif;
+    background-color: #f0f2f5;
+    color: #333;
+    margin: 0;
+    padding: 20px;
+    display: flex;
+    justify-content: center;
+    align-items: flex-start;
+    min-height: 100vh;
+}
+
+.container {
+    width: 100%;
+    max-width: 800px;
+    background-color: #ffffff;
+    border-radius: 8px;
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);
+    overflow: hidden;
+}
+
+header {
+    text-align: center;
+    padding: 24px;
+    border-bottom: 1px solid #e8e8e8;
+}
+
+header h1 {
+    margin: 0 0 8px 0;
+    font-size: 28px;
+    font-weight: 700;
+    color: #1a73e8;
+}
+
+header p {
+    margin: 0;
+    color: #666;
+}
+
+.model-selector {
+    display: flex;
+    justify-content: center;
+    gap: 12px;
+    padding: 24px;
+    background-color: #f7f9fc;
+}
+
+.model-btn {
+    padding: 10px 20px;
+    font-size: 14px;
+    font-weight: 600;
+    border: 1px solid #dcdcdc;
+    border-radius: 20px;
+    background-color: #ffffff;
+    color: #555;
+    cursor: pointer;
+    transition: all 0.2s ease-in-out;
+}
+
+.model-btn:hover {
+    background-color: #f1f3f4;
+    border-color: #c0c0c0;
+}
+
+.model-btn.active {
+    background-color: #1a73e8;
+    color: #ffffff;
+    border-color: #1a73e8;
+}
+
+.model-btn.disabled {
+    background-color: #f5f5f5;
+    color: #b0b0b0;
+    cursor: not-allowed;
+    border-color: #e0e0e0;
+}
+
+main {
+    padding: 32px;
+}
+
+.model-interface {
+    display: none;
+}
+
+.model-interface.active {
+    display: block;
+}
+
+form {
+    background-color: #ffffff;
+    border: 1px solid #e8e8e8;
+    border-radius: 8px;
+    padding: 24px;
+}
+
+form h2 {
+    margin-top: 0;
+    margin-bottom: 8px;
+    font-size: 20px;
+}
+
+form p {
+    margin-top: 0;
+    margin-bottom: 24px;
+    color: #666;
+}
+
+label {
+    display: block;
+    font-weight: 600;
+    margin-bottom: 8px;
+}
+
+textarea, input[type="file"] {
+    width: 100%;
+    box-sizing: border-box;
+}
+
+textarea {
+    padding: 12px;
+    border: 1px solid #dcdcdc;
+    border-radius: 6px;
+    font-size: 14px;
+    min-height: 120px;
+    resize: vertical;
+}
+
+.predict-btn {
+    width: 100%;
+    padding: 12px;
+    font-size: 16px;
+    font-weight: 600;
+    border: none;
+    border-radius: 6px;
+    background-color: #28a745;
+    color: #ffffff;
+    cursor: pointer;
+    margin-top: 24px;
+    transition: background-color 0.2s;
+}
+
+.predict-btn:hover {
+    background-color: #218838;
+}
+
+.upload-label {
+    padding: 12px;
+    border: 1px dashed #dcdcdc;
+    border-radius: 6px;
+    text-align: center;
+    cursor: pointer;
+    display: block;
+    margin-bottom: 16px;
+}
+
+input[type="file"] {
+    display: none;
+}
+
+#image-preview {
+    margin-top: 16px;
+    text-align: center;
+}
+
+#image-preview img {
+    max-width: 100%;
+    max-height: 200px;
+    border-radius: 6px;
+}
+
+.result-display {
+    margin-top: 24px;
+    padding: 16px;
+    border-radius: 6px;
+    background-color: #f7f9fc;
+    border: 1px solid #e8e8e8;
+    text-align: center;
+    font-size: 18px;
+    font-weight: 600;
+    display: none;
+}
+
+.result-display.loading {
+    color: #555;
+}
+
+.result-display.success {
+    color: #1a73e8;
+}
+
+.result-display.error {
+    color: #d93025;
+}