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.env

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+GOOGLE_API_KEY ="AIzaSyASX72XnwUqqM7XZOPY0pxoxqPNRC9thWA"

app.py

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+import streamlit as st
+import numpy as np
+from io import BytesIO
+from PIL import Image
+import tensorflow as tf
+import base64
+import cv2
+import os
+from dotenv import load_dotenv
+import google.generativeai as genai
+load_dotenv()
+genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
+
+header_image_path = 'farmi.jpg'
+st.image(header_image_path, use_column_width='auto')
+
+
+def get_gemini_repsonse(input,prompt):
+    model=genai.GenerativeModel('gemini-pro')
+    response=model.generate_content([input,prompt])
+    return response.text
+
+input_prompt= """You are an farming expert and i want some remedial and preventive information about given tomato plant disease. give me remedial informaion for appropriate environmental condition , soil condition and what pesticides and fertilizers to use. give the information in such away that it is easy for a farmer to understand  if possible in hindi"""
+
+MODEL = tf.keras.models.load_model('./potato_trained_models/1/')
+TOMATO_MODEL = tf.keras.models.load_model('./tomato_trained_models/1')
+PEEPER_MODEL = tf.keras.models.load_model('./pepper_trained_models/1')
+
+class_names = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+
+Tomato_classes = ['Tomato_healthy', 'Tomato_Spider_mites_Two_spotted_spider_mite', 'Tomato__Target_Spot', 'Tomato_Septoria_leaf_spot',
+ 'Tomato__Tomato_mosaic_virus', 'Tomato_Leaf_Mold', 'Tomato_Bacterial_spot', 'Tomato_Late_blight',
+ 'Tomato_Early_blight', 'Tomato__Tomato_YellowLeaf__Curl_Virus']
+
+pepper_classes = ['pepper_bell_bacterial_spot','pepper_healthy']
+
+
+st.title("Plant Disease Detection")
+st.write("This application is detecting disease in three plants photato, tomato and pepper")
+options = ["Select One Plant","Tomato", "Potato", "Pepper"]
+
+
+selected_option = st.selectbox("Select Plant:", options)
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+def read_file_as_image(data)->np.array:
+    image = np.array(data)
+    image = cv2.resize(image, (256,256))
+    return image
+
+async def potato():
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", width=250)
+        image = read_file_as_image(image)
+        image_batch = np.expand_dims(image, axis=0)
+        predictions = MODEL.predict(image_batch)
+        predicted_class = class_names[np.argmax(predictions[0])]
+        confidence = np.max(predictions[0])
+        print("prediction", class_names[np.argmax(predictions)])
+        st.write("Predicted Class : ", predicted_class, " Confidence Level : ", confidence)
+        input=st.text_input(predicted_class,key="input")
+        response=get_gemini_repsonse(input_prompt,input)
+        st.subheader("The Response is")
+        st.write(response)
+
+async def tomato():
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", width=250)
+        image = read_file_as_image(image)
+        image_batch = np.expand_dims(image, axis=0)
+        predictions = TOMATO_MODEL.predict(image_batch)
+        predicted_class = Tomato_classes[np.argmax(predictions[0])]
+        confidence = np.max(predictions[0])
+        print("prediction", Tomato_classes[np.argmax(predictions)])
+        st.write("Predicted Class : ", predicted_class, " Confidence Level : ", confidence)
+        input=st.text_input(predicted_class,key="input")
+        response=get_gemini_repsonse(input_prompt,input)
+        st.subheader("The Response is")
+        st.write(response)
+
+
+async def pepper():
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", width=250)
+        image = read_file_as_image(image)
+        image_batch = np.expand_dims(image, axis=0)
+        predictions = PEEPER_MODEL.predict(image_batch)
+        predicted_class = pepper_classes[np.argmax(predictions[0])]
+        confidence = np.max(predictions[0])
+        print("prediction", pepper_classes[np.argmax(predictions)])
+        st.write("Predicted Class : ", predicted_class, "Confidence Level : ", confidence)
+        input=st.text_input(predicted_class,key="input")
+        response=get_gemini_repsonse(input_prompt,input)
+        st.subheader("The Response is")
+        st.write(response)
+
+
+
+import asyncio
+
+if __name__ == "__main__":
+     if st.button('Predict'):
+    
+        if selected_option == 'Potato':
+            asyncio.run(potato())
+        elif selected_option == 'Tomato':
+            asyncio.run(tomato())
+        else :
+            asyncio.run(pepper())
+        # else:
+        #     st.write("not avalible")