gradio app changes

gradio_app.py

@@ -0,0 +1,146 @@
+import gradio as gr
+import openai
+import numpy as np
+import tensorflow as tf
+import keras
+from PIL import Image
+import requests
+import json
+from json import JSONEncoder
+from datetime import datetime
+
+
+myControls = {
+    "ResultControl":None,
+    "Feedback":None,
+    "AdditionalInfo":None
+}
+
+
+dataToSend = {
+    "FileContent":None,
+    "PlantName":None,
+    "Comments":None
+}
+
+imageData = []
+
+class NumpyEncoder(JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.ndarray) :
+            return obj.tolist()
+        return JSONEncoder.default(self,obj)
+    
+
+
+def uploadFile() :
+    global dataToSend
+
+    npArray = np.asarray(imageData)
+    fileContent = json.dumps(npArray, cls=NumpyEncoder)
+    dataToSend["FileContent"] = fileContent
+
+    payload = json.dumps(dataToSend)
+
+
+    r = requests.post("http://127.0.0.1:5000/todb", data=payload)
+    return r
+
+def saveStats(predictionStatus) :
+    d = {
+        'Time': str(datetime.now()),
+        'PredictionStatus':None
+    }
+
+    if predictionStatus == 'Satisfied' :
+        d['PredictionStatus'] = 1
+    else :
+        d['PredictionStatus'] = 0
+
+    r = requests.post("http://127.0.0.1:5000/predictionstats", data=json.dumps(d))
+    return r
+
+    
+def predict(imageToProcess):
+    global imageData
+    reply = "Nothing to display"
+    try :
+    
+        openai.api_key = "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"
+        message = "Mango Plant Diseases" 
+        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
+
+    imageData = imageToProcess
+    print("Image Dimensions", imageData.height, imageData.width)
+
+    return ["No Disease", reply]
+
+def submitFeedback(correctOrWrong, plantName, userData):
+    global dataToSend
+
+    print(correctOrWrong)
+
+
+    if correctOrWrong == "Not Satisfied" :
+        dataToSend["PlantName"] = plantName
+        dataToSend["Comments"] = userData
+        dataToSend["FileContent"] = json.dumps(np.asarray(imageData).tolist())
+        r = uploadFile()
+
+        if r != None :
+            res = json.loads(r.text)
+            gr.Warning("Data Submitted for learning :" + res["Status"])
+        else :
+            gr.Error("Failed to upload the file for learning")
+
+    saveStats(correctOrWrong)
+
+with gr.Blocks(allow_flagging="manual") as app :
+
+    gr.Markdown(
+    """
+        # AI based plant Disease Detection Application
+       
+    """
+    )
+    myControls["ImageInput"] = gr.Image(type="pil")
+
+    controls = []
+
+    myControls["ResultControl"] = gr.Textbox(label='Possible Disease could be ')
+    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)
+
+
+    gr.Markdown()
+
+    myControls["PredictionSelection"] = gr.Radio(["Satisfied", "Not Satisfied"], label="Feedback", info="Are you satisfied with the prediction?")
+    #myControls["Feedback"] = gr.Checkbox(label="Is prediction wrong? If so, please provide the proper classification")
+    myControls["PlantName"] = gr.Textbox(label='Specify the name of the plant')
+    myControls["UserInput"] = gr.Textbox(label='What is the correct classification?')
+    feedbackBtn = gr.Button(value='Submit Feedback')
+    feedbackBtn.click(submitFeedback, inputs =[myControls["PredictionSelection"], myControls["PlantName"], myControls["UserInput"]])
+
+
+    
+
+
+
+
+    app.queue().launch()