Add app files

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from PIL import Image 
+import numpy as np
+
+from huggingface_hub import hf_hub_download
+
+st.title("lung cancer detection")
+st.write("Upload an image of a lung X-ray to detect lung cancer.")
+
+# Download model from Hugging Face model hub
+model_path = hf_hub_download(
+    repo_id="your-username/lung_cancer_model",  # 👈 replace with your actual username and repo
+    filename="lung_cancer_model.keras"
+)
+
+model = tf.keras.models.load_model(model_path)
+#model = load_model('lung_cancer_model.keras')
+#for uploading a image
+
+img = st.file_uploader("Choose a image file", type=["jpg", "jpeg", "png","webp"])   
+# Check if an image file is uploaded
+if img is not None and img.name.endswith(('jpg', 'jpeg', 'png','webp')):
+    # Display the image
+    image = Image.open(img)
+    st.image(image, caption='Uploaded Image', use_container_width=True)
+    # --- Image preprocessing steps ---
+    image = image.resize((256, 256))  # replace with your model’s input size
+    image_array = np.array(image)
+
+    #🎯 Optional: Auto-detect input size
+#You can dynamically get the expected input shape like this:
+#input_size = model.input_shape[1:3]  # (height, width)
+#Image = image.resize(input_size)
+
+
+    if image_array.shape[-1] == 4:  # RGBA to RGB
+        image_array = image_array[:, :, :3]
+
+    image_array = image_array / 255.0  # normalize if model was trained on normalized images
+    image_array = np.expand_dims(image_array, axis=0)  # add batch dimension
+    class_name_map = {
+    "lung_acc": "Adenocarcinoma (Cancerous)",
+    "lung_n": "Normal (Non-Cancerous)",
+    "lung_scc": "Squamous Carcinoma (Cancerous)"
+     }
+     # List must match order in which the model was trained
+    original_class_names = ["lung_acc", "lung_n", "lung_scc"]
+
+      # Make prediction
+    prediction = model.predict(image_array)
+    predicted_class = np.argmax(prediction)
+    predicted_key = original_class_names[predicted_class]
+    predicted_label = class_name_map[predicted_key]
+
+# Show result
+    st.success(f"Prediction: {predicted_label} (Confidence: {prediction[0][predicted_class]:.2f})")
+
+    
+
+
+ 
+
+  

requirements.txt

@@ -1,3 +1,7 @@
-altair
+numpy
+tensorflow
 pandas
-streamlit
+scikit-learn
+Image
+streamlit
+huggingface_hub

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])),
-    ))