Update app.py

app.py

@@ -314,6 +314,177 @@
 # )
 # iface.launch()
 
+
+############################### MOST WORKING
+
+# 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 != "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)
+#             print(UserInput)
+#             print("appending")
+#             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})
+#                     # messages.append({"role": "user", "content": max_label})
+
+#                     print("IMAGE messages after appending:", messages)
+
+#                     header = {
+#                         "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36",
+#                         "Content-Type": "application/json",
+#                         "Authorization": f"Bearer {auth}"
+#                     }
+
+#                     try:
+#                         response = requests.post(host, headers=header, json={
+#                             "messages": messages,
+#                             "model": model_llm
+#                         }).json()
+#                         print("RESPONSE TRY",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})
+#                     except:
+#                         print("DOESN'T WORK")
+                        
+#                 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
+
+#         elif Images == "None":
+#             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()
+
+############## TEST
+
 import gradio as gr
 import numpy as np
 import cv2 as cv
@@ -385,6 +556,10 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             print("appending")
             messages.append({"role": "user", "content": UserInput})
 
+            # Pop earlier messages if there are more than 10
+            while len(messages) > 10:
+                messages.pop(0)
+
             for i, label in enumerate(labels):
                 prediction_value = float(prediction[0][i])
                 rounded_value = round(prediction_value, 2)
@@ -402,8 +577,6 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
                 if max_rounded_prediction > 0.5:
                     print("\nWays to dispose of this waste: " + max_label)
                     messages.append({"role": "user", "content": content + " " + max_label})
-                    # messages.append({"role": "user", "content": max_label})
-
                     print("IMAGE messages after appending:", messages)
 
                     header = {
@@ -417,9 +590,8 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
                             "messages": messages,
                             "model": model_llm
                         }).json()
-                        print("RESPONSE TRY",response)
+                        print("RESPONSE TRY", 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})
                     except:
                         print("DOESN'T WORK")
@@ -445,6 +617,10 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
 
             messages.append({"role": "user", "content": UserInput})
 
+            # Pop earlier messages if there are more than 10
+            while len(messages) > 10:
+                messages.pop(0)
+
             headers = {
                 "Content-Type": "application/json",
                 "Authorization": f"Bearer {auth}"
@@ -456,8 +632,6 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             }).json()
 
             reply = response["choices"][0]["message"]["content"]
-            # messages.append({"role": "assistant", "content": reply})
-
             output.append({"Mode": "Chat", "content": reply})
 
             return output
@@ -480,6 +654,7 @@ iface = gr.Interface(
 )
 iface.launch()
 
+
 # import gradio as gr
 # import numpy as np
 # import cv2 as cv