updating fast

app.py

@@ -8,6 +8,99 @@ import requests
 import json
 from json import JSONEncoder
 from datetime import datetime
+import requests
+from os import listdir
+import json
+import os
+import cv2
+import joblib
+from json import JSONEncoder
+from keras.optimizers import Adam
+from keras.preprocessing import image
+from keras.preprocessing.image import img_to_array
+from sklearn.preprocessing import MultiLabelBinarizer
+from sklearn.model_selection import train_test_split
+from datetime import datetime
+from fastapi import FastAPI, Request, Response
+from sklearn.metrics import precision_score, recall_score, f1_score
+import prometheus_client as prom
+# Load your trained model
+app = FastAPI()
+DEFAULT_IMAGE_SIZE = tuple((256, 256))
+N_IMAGES = 100
+# Define the model and its parameters
+#model = joblib.load(filename="plant_leaf_diseases_model.keras", 'utf-8')
+model = tf.keras.models.load_model('plant_leaf_diseases_model.keras')
+#test_dir_img_file_path = os.path.getcwd("imagesfolder")
+test_dir_img_file_path = os.path.join(os.getcwd(),"imagesfolder")
+# Define the metrics
+accuracy = prom.Gauge("accuracy", "precision score for random 50 test samples")
+precision_metric = prom.Gauge("plant_precision", "precision score for random 50 test samples")
+recall_metric = prom.Gauge("plant_recall_score", "recall score for random 50 test samples")
+f1_metric = prom.Gauge("plant_f1_score", "F1 score for random 50 test samples")
+
+# Define the function to make predictions
+#def make_prediction(test_dir_img_file_path):
+#    # Load the test images
+#    image_list = []
+#    for plant_image in os.listdir(test_dir_img_file_path):
+##        if plant_image.endswith(".jpg") or plant_image.endswith(".JPG"):
+#            image_list.append(convert_image_to_array(os.path.join(test_dir_img_file_path, plant_image)))
+#    np_image_list = np.array(image_list, dtype=np.float16) / config.model_config.scaling_factor
+#
+#    # Make predictions
+#    predictions = model.predict(np_image_list, verbose=0)
+
+    # Return the predictions
+#    return {"predictions": predictions, "version": _version}
+def convert_image_to_array(image_dir):
+    try:
+        print(image_dir)
+        image = cv2.imread(image_dir)
+        if image is not None:
+            image = cv2.resize(image, DEFAULT_IMAGE_SIZE)
+            return img_to_array(image)
+        else :
+            return np.array([])
+    except Exception as e:
+        print(f"Error : {e}")
+        return None
+# Define the function to update the metrics
+x_test, y_test = train_test_split(test_dir_img_file_path)
+def update_metrics():
+    # Load the test images
+    image_list = []
+    plant_disease_folder_list = listdir(test_dir_img_file_path)
+    for plant_disease_folder in plant_disease_folder_list:
+        plant_disease_image_list = listdir(f"{test_dir_img_file_path}/{plant_disease_folder}/")
+        for plant_image in plant_disease_image_list[:N_IMAGES]:
+            plant_image_directory = f"{test_dir_img_file_path}/{plant_disease_folder}/{image}"
+        if plant_image_directory.endswith(".jpg") or plant_image_directory.endswith(".JPG"):
+            image_list.append(convert_image_to_array(os.path.join(plant_image_directory)))
+            label_list.append(plant_disease_folder)
+            image_len = len(image_list)
+            print(f"Total number of images: {image_len}")
+    np_image_list = np.array(image_list, dtype=np.float16) / 255.0
+    # Make predictions
+    predictions = model.predict(np_image_list, verbose=0)
+
+    # Calculate the metrics
+    pred = np.argmax(predictions, axis=1)
+    y_test2 = np.argmax(y_test, axis=1)
+    accuracy = accuracy_score(y_true, y_pred)
+    precision = precision_score(y_test2, pred)
+    recall = recall_score(y_test2, predictions)
+    f1 = f1_score(y_test2, predictions)
+
+    # Update the metrics
+    precision_metric.set(precision)
+    recall_metric.set(recall)
+    f1_metric.set(f1)
+
+@app.get("/metrics")
+async def get_metrics():
+    update_metrics()
+    return Response(media_type="text/plain", content= prom.generate_latest())
 
 
 myControls = {
@@ -30,7 +123,7 @@ class NumpyEncoder(JSONEncoder):
         if isinstance(obj, np.ndarray) :
             return obj.tolist()
         return JSONEncoder.default(self,obj)
-    
+
 
 
 def uploadFile() :
@@ -43,7 +136,7 @@ def uploadFile() :
     payload = json.dumps(dataToSend)
 
 
-    r = requests.post("http://127.0.0.1:5000/todb", data=payload)
+    r = requests.post("http://127.0.0.1:5005/todb", data=payload)
     return r
 
 def saveStats(predictionStatus) :
@@ -57,35 +150,54 @@ def saveStats(predictionStatus) :
     else :
         d['PredictionStatus'] = 0
 
-    r = requests.post("http://127.0.0.1:5000/predictionstats", data=json.dumps(d))
+    r = requests.post("http://127.0.0.1:5005/predictionstats", data=json.dumps(d))
     return r
 
-    
+
 def predict(imageToProcess):
     global imageData
+    imagelist = []
+    if imageToProcess == None :
+        gr.Error("No image is given to process")
+        return [None, None]
+
+    imageToProcess.save("./temp/test.jpg")
+    image_array = convert_image_to_array("./temp/test.jpg")
+    imagelist.append(image_array)
+    np_image = np.array(image_array, dtype=np.float16) / 225.0
+    print(np_image.shape)
+    prediction = model.predict(np_image.reshape(1,256, 256, 3))
+    leaf_class_category_mappings = {0:"Apple Apple scab", 1:"Apple Black rot", 2:"Apple Cedar apple rust",3:"Apple healthy", 4:"Background without leaves", 5:"Blueberry healthy", 6:"Cherry Powdery mildew", 7:"Cherry healthy",8:"Corn Cercospora leaf spotGray leaf spot", 9:"Corn Common rust", 10:"Corn Northern Leaf Blight", 11:"Corn healthy",12:"Grape Black rot", 13:"Grape Esca (Black Measles)", 14:"Grape Leaf blight (Isariopsis Leaf Spot)", 15:"Grape healthy",16:"Orange Haunglongbing (Citrus greening)", 17:"Peach Bacterial spot", 18:"Peach healthy",19:"Pepper  bell Bacterial spot",20:"Pepper  bell healthy", 21:"Potato Early blight", 22:"Potato Late blight", 23:"Potato healthy", 24:"Raspberry healthy",25:"Soybean healthy", 26:"Squash Powdery mildew", 27:"Strawberry Leaf scorch", 28:"Strawberry healthy", 29:"Tomato Bacterial spot",30:"Tomato Early blight", 31:"Tomato Late blight", 32:"Tomato Leaf Mold", 33:"Tomato Septoria leaf spot", 34:"Tomato Spider mitesTwo-spotted spider mite",35:"Tomato Target Spot", 36:"Tomato Tomato Yellow Leaf Curl Virus", 37:"Tomato Tomato mosaic virus", 38:"Tomato healthy"}
+    print(prediction)
+    #return ["leaf_class_category_mappings[np.argmax(prediction)]"
+    #return [leaf_class_category_mappings[np.argmax(prediction)], "none"]
+    predicteddisease = leaf_class_category_mappings[np.argmax(prediction)]
     reply = "Nothing to display"
+
     try :
-    
-        #openai.api_key = "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"
-        openai_key = "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"
-        message = "Mango Plant Diseases" 
-        if message: 
+        key1="sk"
+        key2="-ico1dAjc3rZL3"
+        key3="ssVVc4LT3BlbkFJwKTdY4IeCkMtZehqRpSU"
+
+        openai.api_key = key1+key2+key3
+        message = "What is "+predicteddisease+" and how to treat the disease"
+
+        if message:
             messages = []
-            messages.append( 
-                {"role": "user", "content": message}, 
-            ) 
-            chat = openai.ChatCompletion.create( 
-                model="gpt-3.5-turbo", messages=messages 
-            ) 
-        
-        reply = chat.choices[0].message.content 
-    except :
-        pass
+            messages.append(
+                {"role": "user", "content": message},
+            )
+            chat = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo", messages=messages
+            )
+
+        reply = chat.choices[0].message.content
+    except Exception as ex :
+        print(ex)
 
-    imageData = imageToProcess
-    print("Image Dimensions", imageData.height, imageData.width)
 
-    return ["No Disease", reply]
+    imageData = imageToProcess
+    return [predicteddisease, reply]
 
 def submitFeedback(correctOrWrong, plantName, userData):
     global dataToSend
@@ -107,12 +219,12 @@ def submitFeedback(correctOrWrong, plantName, userData):
 
     saveStats(correctOrWrong)
 
-with gr.Blocks() as app :
+with gr.Blocks() as gradioapp :
 
     gr.Markdown(
     """
         # AI based plant Disease Detection Application
-       
+
     """
     )
     myControls["ImageInput"] = gr.Image(type="pil")
@@ -123,7 +235,7 @@ with gr.Blocks() as app :
     myControls["AdditionalInfo"] = gr.TextArea(label='Additional Info')
     controls.append(myControls["ResultControl"])
     controls.append(myControls["AdditionalInfo"])
-    
+
 
     predictBtn = gr.Button(value='Predict')
     predictBtn.click(predict, inputs=[myControls["ImageInput"]], outputs=controls)
@@ -138,10 +250,10 @@ with gr.Blocks() as app :
     feedbackBtn = gr.Button(value='Submit Feedback')
     feedbackBtn.click(submitFeedback, inputs =[myControls["PredictionSelection"], myControls["PlantName"], myControls["UserInput"]])
 
+    #app.queue().launch()
+    gradioapp = gr.mount_gradio_app(app, gradioapp, path="/")
 
-    
-
-
-
-
-    app.queue().launch()
+if __name__ == "__main__":
+    # Use this for debugging purposes only
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)   # Ref: https://www.gradio.app/docs/interface