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+# Use an official Python runtime as a parent image
+FROM python:3.8-slim
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Copy the requirements.txt file into the container at /usr/src/app
+COPY requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of your app's source code from your host to your image filesystem.
+COPY . .
+
+# Streamlit runs on port 8501 by default, make this port available to the world outside this container
+EXPOSE 8501
+
+# Run Streamlit app
+CMD ["streamlit", "run", "app/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app/streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit app
+st.title("Penguin Species Predictor")
+
+# Fetch and display model details
+def fetch_model_details(model_id):
+    response = requests.get(f"https://render-fastapi-ku5n.onrender.com/model/?model_id={model_id}")
+    if response.status_code == 200:
+        model_details = response.json()["model"][0]  
+        st.write("### Selected Model Details")
+        for key, value in model_details.items():
+            st.write(f"{key}: {value}")
+    else:
+        st.error("Failed to fetch model details.")
+
+# Model selection
+model_options = {
+    "Model 1": 101, 
+    "Model 2": 102,
+}
+model_name = st.selectbox("Select a Model", options=list(model_options.keys()))
+model_id = model_options[model_name]
+
+# Display model details for the selected model
+fetch_model_details(model_id)
+
+# User inputs for features
+st.write("## Enter Penguin Features")
+bill_length_mm = st.number_input("Bill Length (mm)", min_value=0.0, format="%.2f")
+bill_depth_mm = st.number_input("Bill Depth (mm)", min_value=0.0, format="%.2f")
+flipper_length_mm = st.number_input("Flipper Length (mm)", min_value=0.0, format="%.2f")
+body_mass_g = st.number_input("Body Mass (g)", min_value=0.0, format="%.2f")
+
+# Predict button
+if st.button("Predict"):
+    # Preparing the payload for the POST request
+    payload = {
+        "model_id": model_id - 100,  # Adjusted field name here
+        "bill_length_mm": bill_length_mm,
+        "bill_depth_mm": bill_depth_mm,
+        "flipper_length_mm": flipper_length_mm,
+        "body_mass_g": body_mass_g
+    }
+    # Making the POST request to the FastAPI prediction endpoint
+    response = requests.post("https://render-fastapi-ku5n.onrender.com/predict/", json=payload)
+    if response.status_code == 200:
+        # Processing and displaying the prediction result
+        prediction = response.json()["prediction"]
+        st.write(f"## Predicted Penguin Species: {prediction}")
+    else:
+        # Handling failed prediction attempts
+        st.error(f"Failed to make prediction. Status code: {response.status_code} Response: {response.text}")

requirements.txt

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+docker==7.0.0
+seaborn==0.13.2
+pandas==2.2.1
+streamlit==1.32.2
+requests==2.31.0