Update app.py

app.py

@@ -483,7 +483,219 @@
 # )
 # iface.launch()
 
-############## TEST
+############## WORKING AS OF THIS MONTH ##############
+
+# import gradio as gr
+# import numpy as np
+# import cv2 as cv
+# import requests
+# import io
+# import time
+# from PIL import Image
+# import os
+# import tensorflow as tf
+# import random
+# import openai
+
+# 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")
+# openai.api_key = os.environ.get("auth")
+# openai.api_base = os.environ.get("host")
+# 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 UserInput.lower() == "clear history":
+#         messages.clear()
+#         messages.append(
+#             {"role": "system", "content": system}
+#         )
+        
+#     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")
+#                 url = requests.get(Images)
+#                 image_data = url.content
+#             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})
+
+#             # Pop earlier messages if there are more than 10
+#             # if UserInput.lower() == "clear history":
+#             #     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)
+#                 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})
+#                     print("IMAGE messages after appending:", messages)
+
+#                     print("Message list of image:", 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()
+                        
+#                         completion = openai.ChatCompletion.create(
+#                             model="gpt-3.5-turbo",
+#                             messages=messages
+#                         )
+        
+            
+#                         # reply = response["choices"][0]["message"]["content"]
+#                         reply = completion.choices[0].message['content']
+#                         # # reply = response["choices"][0]["message"]["content"]
+#                         # reply = response.choices[0].message['content']
+#                         print("RESPONSE TRY", completion)
+
+
+#                         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})
+
+#             # Pop earlier messages if there are more than 10
+#             # if UserInput.lower() == "clear history":
+#             #     while len(messages) > 10:
+#             #         messages.pop(0)
+
+#             headers = {
+#                 "Content-Type": "application/json",
+#                 "Authorization": f"Bearer {auth}"
+#             }
+
+#             try:
+#                 # response = requests.post(host, headers=headers, json={
+#                 #     "messages": messages,
+#                 #     "model": model_llm
+#                 # }).json()
+
+#                 completion = openai.ChatCompletion.create(
+#                     model="gpt-3.5-turbo",
+#                     messages=messages
+#                 )
+
+    
+#                 # reply = response["choices"][0]["message"]["content"]
+#                 reply = completion.choices[0].message['content']
+#                 print("RESPONSE TRY (NO IMAGE)", completion, reply)
+
+#             except:
+#                 reply = "Maximum messages: 15. Please clear your history and Try Again! (No Image)"
+#             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()
+
+
+############## NEW VERSION ##############
 
 import gradio as gr
 import numpy as np
@@ -492,6 +704,7 @@ import requests
 import io
 import time
 from PIL import Image
+import base64
 import os
 import tensorflow as tf
 import random
@@ -507,6 +720,8 @@ auth = os.environ.get("auth")
 auth2 = os.environ.get("auth2")
 openai.api_key = os.environ.get("auth")
 openai.api_base = os.environ.get("host")
+vis_url = os.environ.get("vis_url")
+vis_auth = os.environ.get("vis_auth")
 data = None
 
 np.set_printoptions(suppress=True)
@@ -546,6 +761,49 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             else:
                 pass
 
+            def vision():
+                with open("image.png", "wb") as file1_write:
+                    file1_write.write(image_data)
+        
+                with open("image.png", "rb") as file1_read:
+                    file_content = file1_read.read()
+        
+                image = Image.open(io.BytesIO(file_content))
+            
+                base64_image_str = encode_image(image)
+                    
+                payload = {
+                    "content": [
+                        {
+                            "prompt": user,
+                            "image": base64_image_str,
+                        }
+                    ],
+                    "token": vis_auth,
+                }
+            
+                url = vis_url
+                headers = {"Content-Type": "application/json"}
+            
+                response = requests.post(url, headers=headers, data=json.dumps(payload))
+                results = response.json()
+                results = results["result"]
+            
+                answer_index = results.find("Answer:")
+            
+                if answer_index != -1:
+                    try:
+                        result_text = results[answer_index + len("Answer:"):].strip()
+                        print(result_text)
+                        return result_text
+                    except:
+                        pass
+                else:
+                    return "Answer: not found in the string."
+
+            if "vision" in UserInput.lower():
+                vision()
+
             image = cv.imdecode(np.frombuffer(image_data, np.uint8), cv.IMREAD_COLOR)
             image = cv.resize(image, (224, 224))
             image_array = np.asarray(image)