Update app.py

app.py

@@ -1,3 +1,156 @@
+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")
+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(UserInput, Image, Textbox2, Textbox3):
+    if Textbox3 == code:
+        print("Image:  ", Image)
+        if Image is not None:
+            output = []
+            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
+            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="User Input", type="text"),
+    gr.Image(),
+    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
@@ -12,6 +165,7 @@
 # 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
@@ -30,12 +184,21 @@
 #     {"role": "system", "content": system}
 # ]
 
-# def classify(UserInput, Image, Textbox2, Textbox3):
+# def classify(platform,UserInput, Image, Textbox2, Textbox3):
 #     if Textbox3 == code:
-#         print("Image:  ", Image)
 #         if Image is not None:
 #             output = []
-#             image_data = np.array(Image)
+#             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
@@ -136,8 +299,9 @@
 #         return "Unauthorized"
 
 # user_inputs = [
+#     gr.Textbox(label="Platform", type="text"),
 #     gr.Textbox(label="User Input", type="text"),
-#     gr.Image(),
+#     gr.Textbox(label="Image", type="text"),
 #     gr.Textbox(label="Textbox2", type="text"),
 #     gr.Textbox(label="Textbox3", type="password")
 # ]
@@ -155,8 +319,11 @@
 # import numpy as np
 # import cv2 as cv
 # import requests
-# import time
+# import io
+# from PIL import Image
 # import os
+# import tensorflow as tf
+# import random
 
 # host = os.environ.get("host")
 # code = os.environ.get("code")
@@ -167,14 +334,10 @@
 # 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)
 
+# model = tf.keras.models.load_model('keras_model.h5')
 # data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 
 # with open("labels.txt", "r") as file:
@@ -184,117 +347,115 @@
 #     {"role": "system", "content": system}
 # ]
 
-# def classify(platform,UserInput, Image, Textbox2, Textbox3):
+# def classify(platform, UserInput, Images, Textbox2, Textbox3):
 #     if Textbox3 == code:
-#         if Image is not None:
+#         imageData = None
+#         if Images is not None:
 #             output = []
 #             headers = {
 #                 "Authorization": f"Bearer {auth2}"
 #             }
 #             if platform == "wh":
-#                 get_image = requests.get(Image, headers=headers)
-#                 print(get_image.content)
+#                 get_image = requests.get(Images, headers=headers)
+#                 if get_image.status_code == 200:
+#                     image_data = 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)
+
+#             image = cv.imdecode(np.frombuffer(image_data, np.uint8), cv.IMREAD_COLOR)
+#             image = cv.resize(image, (224, 224))
+#             image_array = np.asarray(image)
 #             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)
             
+#             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()
+
+#                     print(response)
                     
+
 #                     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"
 
@@ -312,168 +473,7 @@
 #     outputs=gr.outputs.JSON(),
 #     title="Classifier",
 # )
-# iface.launch()
-
-
-import gradio as gr
-import numpy as np
-import cv2 as cv
-import requests
-import io
-from PIL import Image
-import os
-import tensorflow as tf
-import random
-
-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
-
-np.set_printoptions(suppress=True)
-
-model = tf.keras.models.load_model('keras_model.h5')
-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, Images, Textbox2, Textbox3):
-    if Textbox3 == code:
-        imageData = None
-        if Images is not None:
-            output = []
-            headers = {
-                "Authorization": f"Bearer {auth2}"
-            }
-            if platform == "wh":
-                get_image = requests.get(Images, headers=headers)
-                if get_image.status_code == 200:
-                    image_data = get_image.content
-            elif platform == "web":
-                print("WEB")
-            else:
-                pass
-
-            image = cv.imdecode(np.frombuffer(image_data, np.uint8), cv.IMREAD_COLOR)
-            image = cv.resize(image, (224, 224))
-            image_array = np.asarray(image)
-            normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-            data[0] = normalized_image_array
-            
-            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}')
-
-                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()
-
-                    print(response)
-                    
-
-                    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)
-
-            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()
+# # iface.launch()
 
 # import gradio as gr
 # import numpy as np