Serving Deep Learning Models via API / Web App

model को train और save कर लिया है — अब बारी है उसे दुनिया के सामने पेश करने की 🎯
यानि model को API या Web App के ज़रिए serve करना।

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🧩 दो मुख्य तरीकें:

तरीका	विवरण	उदाहरण
✅ API-Based	Model को backend में serve करें	Flask / FastAPI
✅ Web App	Model के ऊपर UI बनाएं	Streamlit / Gradio

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🔶 1. ✅ Flask API से Model Serve करना (PyTorch Example)

🛠️ Step-by-step Code

📌 app.py:

from flask import Flask, request, jsonify
import torch
import torch.nn as nn

# Your trained model class
class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.fc1 = nn.Linear(2, 4)
        self.fc2 = nn.Linear(4, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.sigmoid(self.fc2(x))

# Load model
model = MyNet()
model.load_state_dict(torch.load("model_weights.pth"))
model.eval()

# Create Flask app
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    input_tensor = torch.tensor(data["input"]).float()
    with torch.no_grad():
        output = model(input_tensor).item()
    return jsonify({"prediction": round(output)})

if __name__ == "__main__":
    app.run(debug=True)

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📲 Test via curl or Postman:

curl -X POST http://127.0.0.1:5000/predict \
     -H "Content-Type: application/json" \
     -d '{"input": [1.0, 0.0]}'

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🔶 2. ✅ Streamlit Web App (Quick & Visual)

📌 app.py

import streamlit as st
import torch
import torch.nn as nn

# Load model
class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.fc1 = nn.Linear(2, 4)
        self.fc2 = nn.Linear(4, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.sigmoid(self.fc2(x))

model = MyNet()
model.load_state_dict(torch.load("model_weights.pth"))
model.eval()

# UI
st.title("XOR Prediction Model")
x1 = st.slider("x1", 0.0, 1.0, 0.0)
x2 = st.slider("x2", 0.0, 1.0, 0.0)

if st.button("Predict"):
    input_tensor = torch.tensor([[x1, x2]])
    with torch.no_grad():
        output = model(input_tensor).item()
        st.write("Prediction:", round(output))

🔼 Run the App:

streamlit run app.py

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🔶 3. ✅ Gradio Web Interface

import gradio as gr
import torch
import torch.nn as nn

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.fc1 = nn.Linear(2, 4)
        self.fc2 = nn.Linear(4, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.sigmoid(self.fc2(x))

model = MyNet()
model.load_state_dict(torch.load("model_weights.pth"))
model.eval()

def predict(x1, x2):
    input_tensor = torch.tensor([[x1, x2]])
    with torch.no_grad():
        output = model(input_tensor).item()
    return round(output)

gr.Interface(fn=predict, inputs=["slider", "slider"], outputs="text").launch()

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✅ Deployment Options:

Platform	Tool	Suitable For
Local Machine	Flask / Streamlit	Testing, demos
Heroku / Render	Flask + Gunicorn	Cloud API
Hugging Face	Gradio Space	Public demo
AWS/GCP	Docker / API	Production

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📝 Practice Questions:

1. Flask API बनाने के लिए कौन से steps follow होते हैं?
2. Streamlit और Gradio में क्या अंतर है?
3. Model को Web पर serve करने के 3 तरीके क्या हैं?
4. TorchScript model को Flask में कैसे load करेंगे?
5. curl से API call कैसे की जाती है?

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🧠 Summary Table

Method	Interface	Setup Time	UI
Flask	JSON API	Medium	❌
Streamlit	Web UI	Very Low	✅
Gradio	Web UI	Very Low	✅
FastAPI	Fast JSON	Medium-High	❌