Update app.py

app.py

@@ -1,67 +1,115 @@
-from dotenv import load_dotenv
+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow.keras import models
+from tensorflow.keras.preprocessing.image import img_to_array
+import matplotlib.pyplot as plt
+import io
+import base64
 
-load_dotenv() ## load all the environment variables
 
-import streamlit as st
-import os
-import google.generativeai as genai
-from PIL import Image
+def inference(image, model_choice):
+    label_map = {'cassava-healthy': 0, 'cassava-not-healthy:bacteria blight': 1}
+    inverse_map = {v: k for k, v in label_map.items()}
+
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    image = image.resize((64, 64))
+    image_arr = img_to_array(image)
+    image_arr /= 255
+    image_arr = image_arr[np.newaxis, :]
 
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+    if model_choice == "Cassava Model 🍃":
+        model = models.load_model("cassava_model.keras")
+    else:
+        model = models.load_model("/content/maize_model.keras")
+    proba = model.predict(image_arr)
+    label = (proba > 0.5).squeeze().astype(int)
 
-## Function to load Google Gemini Pro Vision API And get response
+    result = {
+        "label": inverse_map.get(int(label)),
+        "probability": float(proba.squeeze())
+    }
 
-def get_gemini_repsonse(input,image,prompt):
-    model=genai.GenerativeModel('gemini-pro-vision')
-    response=model.generate_content([input,image[0],prompt])
-    return response.text
+    # Create visualization
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.bar(['Healthy 🌿', 'Not Healthy 🍂'], [1 - result['probability'], result['probability']], color=['#2ecc71', '#e74c3c'])
+    ax.set_ylim(0, 1)
+    ax.set_ylabel('Probability')
+    ax.set_title('Plant Health Prediction 🔍', fontsize=16, fontweight='bold')
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    plt.tight_layout()
 
-def input_image_setup(uploaded_file):
-    # Check if a file has been uploaded
-    if uploaded_file is not None:
-        # Read the file into bytes
-        bytes_data = uploaded_file.getvalue()
+    # Convert plot to image
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plot_image = Image.open(buf)
 
-        image_parts = [
-            {
-                "mime_type": uploaded_file.type,  # Get the mime type of the uploaded file
-                "data": bytes_data
-            }
-        ]
-        return image_parts
-    else:
-        raise FileNotFoundError("No file uploaded")
-    
-##initialize our streamlit app
-
-st.set_page_config(page_title="Crop Disease Detection App")
-
-st.header("Gemini Crop Disease App")
-input=st.text_input("Input Prompt: ",key="input")
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-image=""   
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
-
-
-submit=st.button("Predict Crop/Plant Health")
-
-input_prompt="""
-        "You are an expert in computer vision and agriculture who can easily predict the disease of the plant. "
-        "Analyze the following image and provide 6 outputs in a structured table format: "
-        "1. Crop in the image, "
-        "2. Whether it is infected or healthy, "
-        "3. Type of disease (if any), "
-        "4. How confident out of 100% whether image is healthy or infected "
-        "5. Reason for the disease such as whether it is happening due to fungus, bacteria, insect bite, poor nutrition, etc., "
-        "6. Precautions for it."
+    return result["label"], f"{result['probability']:.2%} of illness(bacteria blight)", plot_image
+
+# Custom CSS for styling
+custom_css = """
+#component-0 {
+    max-width: 730px;
+    margin: auto;
+    padding: 1.5rem;
+    border-radius: 10px;
+    background: linear-gradient(135deg, #f6d365 0%, #fda085 100%);
+    box-shadow: 0 10px 20px rgba(0,0,0,0.19), 0 6px 6px rgba(0,0,0,0.23);
+}
+#component-1 {
+    border-radius: 10px;
+    overflow: hidden;
+}
+#component-5 {
+    border-radius: 10px;
+    overflow: hidden;
+    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+}
+.label {
+    font-size: 18px !important;
+    color: #2c3e50;
+    font-weight: bold;
+}
+.output-class {
+    font-size: 24px !important;
+    color: #2980b9;
+    font-weight: bold;
+}
+.output-prob {
+    font-size: 20px !important;
+    color: #16a085;
+}
 """
 
-## If submit button is clicked
+# Gradio interface
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("# 🌱 Crop Diseases Detector 🕵️‍♂️")
+    gr.Markdown("Upload an image of a cassava plant and let's check its health!")
+
+    with gr.Row():
+        input_image = gr.Image(type="numpy", label="📸 Upload or Capture Image")
+        output_image = gr.Image(type="pil", label="🖼️ Health Prediction Visualization")
+
+    model_choice = gr.Dropdown(["Cassava Model 🍃"], label="🤖 Select Model", value="Cassava Model 🍃")
+
+    with gr.Row():
+        detect_btn = gr.Button("🔍 Detect Plant Health", variant="primary")
+
+    output_label = gr.Textbox(label="🏷️ Diagnosis")
+    output_confidence = gr.Textbox(label="📊 Confidence")
+
+    detect_btn.click(
+        inference,
+        inputs=[input_image, model_choice],
+        outputs=[output_label, output_confidence, output_image]
+    )
+
+    gr.Markdown("## How to use:")
+    gr.Markdown("1. 📤 Upload an image or 📸 take a picture of a cassava plant")
+    gr.Markdown("2. 🤖 Select the model you want to use")
+    gr.Markdown("3. 🔍 Click 'Detect Plant Health' to get the results")
+    gr.Markdown("4. 📊 View the diagnosis, confidence score, and health prediction chart")
 
-if submit:
-    image_data=input_image_setup(uploaded_file)
-    response=get_gemini_repsonse(input_prompt,image_data,input)
-    st.subheader("The Response is")
-    st.write(response)
+demo.launch(debug=True)