Upload pdd1.py

pdd1.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+
+import io
+
+
+import google.generativeai as genai
+
+def app():
+    def import_and_predict(image_data, model, class_labels):
+        size = (256, 256)
+
+        if image_data is not None:
+            image = Image.open(io.BytesIO(image_data.read()))
+            image = image.resize(size)
+            image = np.array(image)
+            img_reshape = image / 255.0
+            img_reshape = np.expand_dims(img_reshape, axis=0)
+
+            prediction = model.predict(img_reshape)
+            st.image(image, width=300)
+            predictions_label = class_labels[np.argmax(prediction[0])]
+            return predictions_label
+        else:
+            st.warning("Please upload an image.")
+            return None
+
+
+    def get_info_from_gemini(prompt):
+        genai.configure(api_key=os.environ.get('gemini_api'))
+        model = genai.GenerativeModel('gemini-pro')
+        response = model.generate_content(f"{prompt}")
+        return response
+
+
+
+
+    st.title("Plant Disease Detection")
+
+    uploaded_image = st.file_uploader(f"Upload an image", type=["jpg", "jpeg", "png"])
+    models_path = ['./best_model_100_subset.h5',]
+
+
+
+    CLASS_LABELS = ['Tomato Early blight', 'Tomato Leaf Mold', 'Tomato YellowLeaf Curl Virus',
+                                    'Tomato mosaic virus', 'Tomato healthy']
+                    
+    model = tf.keras.models.load_model(models_path[0])
+
+
+
+
+    prediction = import_and_predict(uploaded_image, model, CLASS_LABELS)
+    st.write("disease name: ", prediction)
+
+
+    if prediction != None:
+                new_title = '<p style="font-size: 38px">Measures you can take to control: </p>'
+                st.markdown(new_title, unsafe_allow_html=True)
+                if prediction == CLASS_LABELS[4]:
+                    st.write("Plant is healthy take good care of it")
+                response = get_info_from_gemini(f"cure for the disease {prediction} tell in bulletpoints and estimated cost in inr at last give summary of each measures estimated cost")
+                st.write(response.text)
+