Update app.py

app.py

@@ -402,6 +402,8 @@ 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 = {
@@ -415,12 +417,12 @@ 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 Exception as e:
-                        print("ERROR:", e)
+                    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"})
@@ -448,18 +450,15 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
                 "Authorization": f"Bearer {auth}"
             }
 
-            try:
-                response = requests.post(host, headers=headers, json={
-                    "messages": messages,
-                    "model": model_llm
-                }).json()
+            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})
+            reply = response["choices"][0]["message"]["content"]
+            messages.append({"role": "assistant", "content": reply})
 
-                output.append({"Mode": "Chat", "content": reply})
-            except Exception as e:
-                print("ERROR:", e)
+            output.append({"Mode": "Chat", "content": reply})
 
             return output
     else:
@@ -481,173 +480,6 @@ iface = gr.Interface(
 )
 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 != "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()
-
 # import gradio as gr
 # import numpy as np
 # import cv2 as cv