first commit

Dockerfile

@@ -18,4 +18,4 @@ EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "src/app.py", "--server.port=8501", "--server.address=0.0.0.0"]

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+tensorflow
+keras
+numpy
+matplotlib

src/FishModel_VGG16.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17d0cf24c5ba0619e68137db06d5a80cca1b369fcc07d605a0b4c8d98a62f558
+size 68281896

src/app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+from keras.models import load_model
+from keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+
+# Load model
+model = load_model('FishModel_VGG16.h5', compile=False)
+
+# Class labels
+class_names = ['Bangus', 'Big Head Carp', 'Black Spotted Barb', 'Catfish', 'Climbing Perch', 
+               'Fourfinger Threadfin','Freshwater Eel', 'Glass Perchlet', 'Goby', 'Gold Fish', 
+               'Gourami', 'Grass Carp', 'Green Spotted Puffer', 'Indian Carp', 'Indo-Pacific Tarpon', 
+               'Jaguar Gapote', 'Janitor Fish', 'Knifefish', 'Long-Snouted Pipefish','Mosquito Fish', 
+               'Mudfish', 'Mullet', 'Pangasius', 'Perch', 'Scat Fish', 'Silver Barb', 'Silver Carp', 
+               'Silver Perch', 'Snakehead', 'Tenpounder', 'Tilapia']
+
+# App Title
+st.title("🐟 Fish Classifier App")
+st.subheader("Identify fish species using a VGG16-based deep learning model.")
+
+# Instructions
+st.markdown("""
+### 📌 How to Use
+1. **Upload** a clear image of a fish (supported formats: JPG, JPEG, PNG).
+2. The app will automatically **analyze the image** using a trained deep learning model.
+3. You will get the **predicted fish species** along with the **confidence level**.
+
+💡 *Tip: Use centered and well-lit fish images for better results.*
+""")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload an image of a fish", type=["jpg", "jpeg", "png"])
+
+# Prediction logic
+def predict_image(img_path):
+    img = load_img(img_path, target_size=(224, 224))
+    img_array = img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    preds = model.predict(img_array)
+    pred_class = class_names[np.argmax(preds)]
+    confidence = np.max(preds)
+    return pred_class, confidence
+
+# Handle uploaded file
+if uploaded_file is not None:
+    st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
+    with open("temp.jpg", "wb") as f:
+        f.write(uploaded_file.read())
+    label, conf = predict_image("temp.jpg")
+    st.success(f"Prediction: **{label}** ({conf * 100:.2f}% confidence)")

src/model_weights_VGG16.weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c05ec07b94425d5c63a042ed6df3f1755392e0f318b73f379df8f91e8e02edd0
+size 68257184

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))