Update app.py

app.py

@@ -151,12 +151,176 @@
 # iface.launch()
 
 
+# import gradio as gr
+# import numpy as np
+# import cv2 as cv
+# import requests
+# import time
+# import os
+
+# host = os.environ.get("host")
+# code = os.environ.get("code")
+# model_llm = os.environ.get("model")
+# content = os.environ.get("content")
+# state = os.environ.get("state")
+# system = os.environ.get("system")
+# auth = os.environ.get("auth")
+# auth2 = os.environ.get("auth2")
+# data = None
+# model = None
+# image = None
+# prediction = None
+# labels = None
+
+# print('START')
+# np.set_printoptions(suppress=True)
+
+# data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+# with open("labels.txt", "r") as file:
+#     labels = file.read().splitlines()
+
+# messages = [
+#     {"role": "system", "content": system}
+# ]
+
+# def classify(platform,UserInput, Image, Textbox2, Textbox3):
+#     if Textbox3 == code:
+#         if Image is not None:
+#             output = []
+#             headers = {
+#                 "Authorization": f"Bearer {auth2}"
+#             }
+#             if platform == "wh":
+#                 get_image = requests.get(Image, headers=headers)
+#                 print(get_image.content)
+#             elif platform == "web":
+#                 print("WEB")
+#             else:
+#                 pass
+#             image_data = np.array(get_image)
+#             image_data = cv.resize(image_data, (224, 224))
+#             image_array = np.asarray(image_data)
+#             normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+#             data[0] = normalized_image_array
+        
+#             import tensorflow as tf
+#             model = tf.keras.models.load_model('keras_model.h5')
+        
+#             prediction = model.predict(data)
+            
+#             max_label_index = None
+#             max_prediction_value = -1
+    
+#             print('Prediction')
+    
+#             Textbox2 = Textbox2.replace("[", "").replace("]", "").replace("'", "")
+#             Textbox2 = Textbox2.split(",")
+#             Textbox2_edited = [x.strip() for x in Textbox2]
+#             Textbox2_edited = list(Textbox2_edited)
+#             Textbox2_edited.append(UserInput)
+#             messages.append({"role": "user", "content": UserInput})
+        
+#             for i, label in enumerate(labels):
+#                 prediction_value = float(prediction[0][i])
+#                 rounded_value = round(prediction_value, 2)
+#                 print(f'{label}: {rounded_value}')
+        
+#                 if prediction_value > max_prediction_value:
+#                     max_label_index = i
+#                     max_prediction_value = prediction_value 
+        
+#             if max_label_index is not None:
+#                 max_label = labels[max_label_index].split(' ', 1)[1]
+#                 max_rounded_prediction = round(max_prediction_value, 2)
+#                 print(f'Maximum Prediction: {max_label} with a value of {max_rounded_prediction}')
+    
+#                 time.sleep(1)
+#                 if max_rounded_prediction > 0.5:
+#                     print("\nWays to dispose of this waste: " + max_label)
+#                     messages.append({"role": "user", "content": content + " " + max_label})
+        
+#                     headers = {
+#                         "Content-Type": "application/json",
+#                         "Authorization": f"Bearer {auth}"
+#                     }
+            
+#                     response = requests.post(host, headers=headers, json={
+#                         "messages": messages,
+#                         "model": model_llm
+#                     }).json()
+                    
+#                     reply = response["choices"][0]["message"]["content"]
+#                     messages.append({"role": "assistant", "content": reply})
+                    
+#                     output.append({"Mode": "Image", "type": max_label, "prediction_value": max_rounded_prediction, "content": reply})
+#                 elif max_rounded_prediction < 0.5:
+#                     output.append({"Mode": "Image", "type": "Not predictable", "prediction_value": max_rounded_prediction, "content": "Seems like the prediction rate is too low due to that won't be able to predict the type of material. Try again with a cropped image or different one."})
+            
+#             return output
+
+#         else:
+#             output = []
+            
+#             Textbox2 = Textbox2.replace("[", "").replace("]", "").replace("'", "")
+#             Textbox2 = Textbox2.split(",")
+#             Textbox2_edited = [x.strip() for x in Textbox2]
+#             Textbox2_edited = list(Textbox2_edited)
+#             Textbox2_edited.append(UserInput)
+        
+#             for i in Textbox2_edited:
+#                 messages.append(
+#                     {"role": "user", "content": i}
+#             )
+            
+#             print("messages after appending:", messages)
+        
+#             time.sleep(1)
+#             messages.append({"role": "user", "content": UserInput})
+
+#             headers = {
+#                 "Content-Type": "application/json",
+#                 "Authorization": f"Bearer {auth}"
+#             }
+    
+#             response = requests.post(host, headers=headers, json={
+#                 "messages": messages,
+#                 "model": model_llm
+#             }).json()
+            
+#             reply = response["choices"][0]["message"]["content"]
+#             messages.append({"role": "assistant", "content": reply})
+
+#             output.append({"Mode": "Chat", "content": reply})
+            
+#             return output
+
+#     else:
+#         return "Unauthorized"
+
+# user_inputs = [
+#     gr.Textbox(label="Platform", type="text"),
+#     gr.Textbox(label="User Input", type="text"),
+#     gr.Textbox(label="Image", type="text"),
+#     gr.Textbox(label="Textbox2", type="text"),
+#     gr.Textbox(label="Textbox3", type="password")
+# ]
+
+# iface = gr.Interface(
+#     fn=classify,
+#     inputs=user_inputs,
+#     outputs=gr.outputs.JSON(),
+#     title="Classifier",
+# )
+# iface.launch()
+
+
 import gradio as gr
 import numpy as np
 import cv2 as cv
 import requests
-import time
 import os
+import tensorflow as tf
 
 host = os.environ.get("host")
 code = os.environ.get("code")
@@ -166,17 +330,13 @@ state = os.environ.get("state")
 system = os.environ.get("system")
 auth = os.environ.get("auth")
 auth2 = os.environ.get("auth2")
-data = None
-model = None
-image = None
-prediction = None
-labels = None
 
-print('START')
 np.set_printoptions(suppress=True)
 
-data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+# Load the model outside of the function
+model = tf.keras.models.load_model('keras_model.h5')
 
+# Load labels from a file
 with open("labels.txt", "r") as file:
     labels = file.read().splitlines()
 
@@ -184,7 +344,7 @@ messages = [
     {"role": "system", "content": system}
 ]
 
-def classify(platform,UserInput, Image, Textbox2, Textbox3):
+def classify(platform, UserInput, Image, Textbox2, Textbox3):
     if Textbox3 == code:
         if Image is not None:
             output = []
@@ -193,108 +353,100 @@ def classify(platform,UserInput, Image, Textbox2, Textbox3):
             }
             if platform == "wh":
                 get_image = requests.get(Image, headers=headers)
-                print(get_image.content)
+                image_data = cv.imdecode(np.asarray(bytearray(get_image.content), dtype="uint8"), cv.IMREAD_COLOR)
             elif platform == "web":
                 print("WEB")
+                # Handle web case if needed
             else:
                 pass
-            image_data = np.array(Image)
+
             image_data = cv.resize(image_data, (224, 224))
-            image_array = np.asarray(image_data)
-            normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+            normalized_image_array = (image_data.astype(np.float32) / 127.0) - 1
             data[0] = normalized_image_array
-        
-            import tensorflow as tf
-            model = tf.keras.models.load_model('keras_model.h5')
-        
+
             prediction = model.predict(data)
-            
+
             max_label_index = None
             max_prediction_value = -1
-    
+
             print('Prediction')
-    
+
             Textbox2 = Textbox2.replace("[", "").replace("]", "").replace("'", "")
             Textbox2 = Textbox2.split(",")
             Textbox2_edited = [x.strip() for x in Textbox2]
             Textbox2_edited = list(Textbox2_edited)
             Textbox2_edited.append(UserInput)
             messages.append({"role": "user", "content": UserInput})
-        
+
             for i, label in enumerate(labels):
                 prediction_value = float(prediction[0][i])
                 rounded_value = round(prediction_value, 2)
                 print(f'{label}: {rounded_value}')
-        
+
                 if prediction_value > max_prediction_value:
                     max_label_index = i
-                    max_prediction_value = prediction_value 
-        
+                    max_prediction_value = prediction_value
+
             if max_label_index is not None:
                 max_label = labels[max_label_index].split(' ', 1)[1]
                 max_rounded_prediction = round(max_prediction_value, 2)
                 print(f'Maximum Prediction: {max_label} with a value of {max_rounded_prediction}')
-    
-                time.sleep(1)
+
                 if max_rounded_prediction > 0.5:
                     print("\nWays to dispose of this waste: " + max_label)
                     messages.append({"role": "user", "content": content + " " + max_label})
-        
+
                     headers = {
                         "Content-Type": "application/json",
                         "Authorization": f"Bearer {auth}"
                     }
-            
+
                     response = requests.post(host, headers=headers, json={
                         "messages": messages,
                         "model": model_llm
                     }).json()
-                    
+
                     reply = response["choices"][0]["message"]["content"]
                     messages.append({"role": "assistant", "content": reply})
-                    
+
                     output.append({"Mode": "Image", "type": max_label, "prediction_value": max_rounded_prediction, "content": reply})
                 elif max_rounded_prediction < 0.5:
-                    output.append({"Mode": "Image", "type": "Not predictable", "prediction_value": max_rounded_prediction, "content": "Seems like the prediction rate is too low due to that won't be able to predict the type of material. Try again with a cropped image or different one."})
-            
+                    output.append({"Mode": "Image", "type": "Not predictable", "prediction_value": max_rounded_prediction, "content": "Seems like the prediction rate is too low due to that won't be able to predict the type of material. Try again with a cropped image or different one"})
+
             return output
 
         else:
             output = []
-            
+
             Textbox2 = Textbox2.replace("[", "").replace("]", "").replace("'", "")
             Textbox2 = Textbox2.split(",")
             Textbox2_edited = [x.strip() for x in Textbox2]
             Textbox2_edited = list(Textbox2_edited)
             Textbox2_edited.append(UserInput)
-        
+
             for i in Textbox2_edited:
-                messages.append(
-                    {"role": "user", "content": i}
-            )
-            
+                messages.append({"role": "user", "content": i})
+
             print("messages after appending:", messages)
-        
-            time.sleep(1)
+
             messages.append({"role": "user", "content": UserInput})
 
             headers = {
                 "Content-Type": "application/json",
                 "Authorization": f"Bearer {auth}"
             }
-    
+
             response = requests.post(host, headers=headers, json={
                 "messages": messages,
                 "model": model_llm
             }).json()
-            
+
             reply = response["choices"][0]["message"]["content"]
             messages.append({"role": "assistant", "content": reply})
 
             output.append({"Mode": "Chat", "content": reply})
-            
-            return output
 
+            return output
     else:
         return "Unauthorized"