Create app.py

app.py

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+!pip install -q -U google-generativeai
+
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from numpy import asarray
+import gradio as gr
+import google.generativeai as genai
+#import cv2
+
+print("Dependency Imported Successfully!")
+
+
+GOOGLE_API_KEY = "AIzaSyA4pL0voDE0py8q8iXtQNQRYYMx_UdFeLk"
+genai.configure(api_key=GOOGLE_API_KEY)
+model = genai.GenerativeModel('gemini-pro')
+
+
+#kindly add model path here (Save model in your drive and copy path and paste it below)
+model_path="./Models/"
+
+sugarcane_model = tf.keras.models.load_model(f"{model_path}sugracane.h5")
+tomato_model = tf.keras.models.load_model(f"{model_path}Tomato.h5")
+corn_model = tf.keras.models.load_model(f"{model_path}Corn.h5")
+potato_model = tf.keras.models.load_model(f"{model_path}Potato.h5")
+rice_model = tf.keras.models.load_model(f"{model_path}Rice.h5")
+wheat_model = tf.keras.models.load_model(f"{model_path}Wheat.h5")
+
+
+print("Models Imported Successfully!")
+
+pred_class = "Plant"
+
+
+def predict(model,class_name,img):
+  global pred_class
+  img = tf.image.resize(img, (256, 256))
+  img_array = tf.keras.preprocessing.image.img_to_array(img)
+  img_array = tf.expand_dims(img_array, 0)
+  predictions = model.predict(img_array)
+  predicted_class = class_name[np.argmax(predictions[0])]
+  confidence = round(100 * (np.max(predictions[0])), 2)
+  pred_class = predicted_class
+  return predicted_class,f"{confidence} %"
+
+
+
+
+def generate_output(crop,Leaf_Image):
+  if crop=="Sugarcane":
+    sugarcane_classes = ['Sugarcane___Bacterial_Blight', 'Sugarcane___Healthy', 'Sugarcane___Red_Rot']
+    #sugarcane_model = tf.keras.models.load_model(f"{model_path}sugracane.h5")
+    return predict(sugarcane_model,sugarcane_classes,Leaf_Image)
+
+  if crop=="Tomato":
+    tomato_classes = ['Tomato___Bacterial_spot','Tomato___Early_blight','Tomato___Late_blight','Tomato___healthy']
+    #tomato_model = tf.keras.models.load_model(f"{model_path}Tomato.h5")
+    return predict(tomato_model,tomato_classes,Leaf_Image)
+
+  if crop=="Corn":
+    corn_classes = ['Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot','Corn_(maize)___Common_rust_','Corn_(maize)___Northern_Leaf_Blight','Corn_(maize)___healthy']
+    #corn_model = tf.keras.models.load_model(f"{model_path}Corn.h5")
+    return predict(corn_model,corn_classes,Leaf_Image)
+
+  if crop=="Potato":
+    potato_classes = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+    #potato_model = tf.keras.models.load_model(f"{model_path}Potato.h5")
+    return predict(potato_model,potato_classes,Leaf_Image)
+
+  if crop=="Rice":
+    rice_classes = ['Rice___Brown_Spot', 'Rice___Healthy', 'Rice___Hispa', 'Rice___Leaf_Blast']
+    #rice_model = tf.keras.models.load_model(f"{model_path}Rice.h5")
+    return predict(rice_model,rice_classes,Leaf_Image)
+
+  if crop=="Wheat":
+    wheat_classes = ['Wheat___Brown_Rust', 'Wheat___Healthy', 'Wheat___Yellow_Rust']
+    #wheat_model = tf.keras.models.load_model(f"{model_path}Wheat.h5")
+    return predict(wheat_model,wheat_classes,Leaf_Image)
+
+
+def transform_history(history):
+    new_history = []
+    new_history.append({"parts": [{"text": "You are a Agriculture experts named PlantDoc, that specializes in Plant Diseases"}], "role": "user"})
+    new_history.append({"parts": [{"text": "ok"}], "role": "model"})
+    print(history)
+    for chat in history:
+        new_history.append({"parts": [{"text": chat[0]}], "role": "user"})
+        new_history.append({"parts": [{"text": chat[1]}], "role": "model"})
+    return new_history
+
+
+
+def response(message, history):
+    global chat
+    # The history will be the same as in Gradio, the 'Undo' and 'Clear' buttons will work correctly.
+    chat.history = transform_history(history)
+    response = chat.send_message(message)
+    response.resolve()
+
+    # Each character of the answer is displayed
+    for i in range(len(response.text)):
+        time.sleep(0.01)
+        yield response.text[: i+1]
+css = """
+#textbox {height: 700px;}
+"""
+
+with gr.Blocks(css=css) as demo:
+  with gr.Row():
+    with gr.Column():
+      leaf_img = gr.Image(type="numpy",label="Upload Plant's Leaf Image")
+    with gr.Column():
+      crop = gr.Radio(["Potato", "Tomato", "Wheat","Rice","Corn","Sugarcane"], label="Crop", info="Please Select a Crop?")
+      disease = gr.Textbox(label="Disease Predicted :- ")
+      confi = gr.Textbox(label="Level of Condidence:- ")
+  with gr.Row():
+    with gr.Column(elem_id="textbox"):
+      greet_btn = gr.Button("Predict")
+      greet_btn.click(fn=generate_output, inputs=[crop,leaf_img], outputs=[disease,confi], api_name="predict")
+      gr.ChatInterface(fn=response,examples=["Give me more Info about this disease!", "How to treat this Plant disease!"], title="Talk to PlantDoc(AI Expert).")
+if __name__ == "__main__":
+  chat = model.start_chat(history=[])
+  demo.launch()