Diseases with descriptions

requirements.txt

-absl-py==0.14.0
 asgiref==3.4.1
-astunparse==1.6.3
-cachetools==4.2.2
-certifi==2021.5.30
-charset-normalizer==2.0.6
-clang==5.0
 click==8.0.1
 colorama==0.4.4
-dnspython==2.1.0
+dnspython==1.16.0
 fastapi==0.68.1
-flatbuffers==1.12
-gast==0.4.0
-google-auth==1.35.0
-google-auth-oauthlib==0.4.6
-google-pasta==0.2.0
-grpcio==1.40.0
 h11==0.12.0
-h5py==3.1.0
-idna==3.2
-keras==2.6.0
-Keras-Preprocessing==1.1.2
-Markdown==3.3.4
-numpy==1.19.5
-oauthlib==3.1.1
-opt-einsum==3.3.0
-pandas==1.3.3
-Pillow==8.3.2
-protobuf==3.18.0
-pyasn1==0.4.8
-pyasn1-modules==0.2.8
 pydantic==1.8.2
 pymongo==3.12.0
-python-dateutil==2.8.2
 python-multipart==0.0.5
-pytz==2021.1
-requests==2.26.0
-requests-oauthlib==1.3.0
-rsa==4.7.2
-six==1.15.0
+six==1.16.0
 starlette==0.14.2
-tensorboard==2.6.0
-tensorboard-data-server==0.6.1
-tensorboard-plugin-wit==1.8.0
-tensorflow-cpu==2.6.0
-tensorflow-estimator==2.6.0
-termcolor==1.1.0
-typing-extensions==3.7.4.3
-urllib3==1.26.7
+typing-extensions==3.10.0.2
 uvicorn==0.15.0
-Werkzeug==2.0.1
-wrapt==1.12.1
\ No newline at end of file

routes/prediction.py

 from fastapi import APIRouter, File, UploadFile
 from pydantic import BaseModel
-import tensorflow as tf
-from tensorflow import keras 
+# import tensorflow as tf
+# from tensorflow import keras 
 from typing import List
 from os import error
-import pandas as pd
-import numpy as np
+# import pandas as pd
+# import numpy as np
 import json
-from keras.preprocessing import image 
+#from keras.preprocessing import image 
 from config.db import disease_collection
 from schemas.disease import diseasesEntity
 
@@ -57,7 +57,7 @@ async def predict_symptoms(symptoms: Symptoms):
             if score>=threshold:
                 output.append(data.get(key))
         
-        diseases = diseasesEntity(disease_collection.find({"name": {"$in": output}})) 
+        diseases = diseasesEntity(disease_collection.find({"name": {"$in": output}}))  
                 
         return {"success": True, "result": diseases}  
         
@@ -69,30 +69,30 @@ async def predict_symptoms(symptoms: Symptoms):
 @prediction.post('/api/predictions/picture')
 async def predict_symptoms(req_file: UploadFile = File(...)):
     try:
-      img = read_imagefile(req_file)
+      # img = read_imagefile(req_file)
 
-      if img is None:
-        return {"success": False, "msg": 'Not a valid data format'}
+      # if img is None:
+      #   return {"success": False, "msg": 'Not a valid data format'}
 
-      model_path = './xception'
-      image_path = "./temp_images/temp_image.jpg"
+      # model_path = './xception'
+      # image_path = "./temp_images/temp_image.jpg"
 
-      #load model
-      model = keras.models.load_model(model_path)
+      # #load model
+      # model = keras.models.load_model(model_path)
 
-      #load data
-      load_img = image.load_img(image_path, target_size=(img_height, img_width))
-      img_array = image.img_to_array(load_img)
-      img_array = tf.expand_dims(img_array, 0)
+      # #load data
+      # load_img = image.load_img(image_path, target_size=(img_height, img_width))
+      # img_array = image.img_to_array(load_img)
+      # img_array = tf.expand_dims(img_array, 0)
 
-      #predict
-      predictions = model.predict(img_array, batch_size)
-      score = tf.nn.softmax(predictions[0])
+      # #predict
+      # predictions = model.predict(img_array, batch_size)
+      # score = tf.nn.softmax(predictions[0])
 
-      #return
-      prediction = class_names[np.argmax(score)]
+      # #return
+      # prediction = class_names[np.argmax(score)]
 
-      return {"success": True, "result": prediction}
+      return {"success": True, "result": req_file.filename}
       
     except error: 
       return {"success": False, "msg": error}

schemas/disease.py

-def diseaseEntity(item) -> dict:
-    return {
-        "id":str(item["id"]),
+def diseaseEntity(item) -> dict: 
+    return { 
         "name": item["name"],
         "description": item["description"],
     }