Update app.py

app.py

@@ -315,181 +315,15 @@
 # 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)
-
-# # 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()
-
-# 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:
-#                     random_id = random.randint(1000, 9999)
-#                     file_extension = ".png"
-#                     filename = f"image_{random_id}{file_extension}"
-#                     with open(filename, "wb") as file:
-#                         file.write(get_image.content)
-#                     print(f"Saved image as: {filename}")
-
-#                     full_path = os.path.join(os.getcwd(), filename)
-#                     print(f"Saved image as: {full_path}")
-#             elif platform == "web":
-#                 print("WEB")
-#                 # Handle web case if needed
-#             else:
-#                 pass
-
-#             image = cv.imread(full_path)
-#             image = cv.resize(image, (224, 224))
-#             image_array = np.asarray(image)
-#             image_data = cv.resize(imageData, (224, 224))
-#             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()
-
-#                     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()
 import gradio as gr
 import numpy as np
 import cv2 as cv
 import requests
-import random
+import io
+from PIL import Image
 import os
 import tensorflow as tf
-import base64
+import random
 
 host = os.environ.get("host")
 code = os.environ.get("code")
@@ -510,12 +344,13 @@ model = tf.keras.models.load_model('keras_model.h5')
 with open("labels.txt", "r") as file:
     labels = file.read().splitlines()
 
-messages = [{"role": "system", "content": system}]
+messages = [
+    {"role": "system", "content": system}
+]
 
 def classify(platform, UserInput, Images, Textbox2, Textbox3):
     if Textbox3 == code:
         imageData = None
-        image_data_url = None  # Initialize image_data_url
         if Images is not None:
             output = []
             headers = {
@@ -524,80 +359,76 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             if platform == "wh":
                 get_image = requests.get(Images, headers=headers)
                 if get_image.status_code == 200:
-                    # Convert the image data to base64
-                    image_base64 = base64.b64encode(get_image.content).decode("utf-8")
-
-                    # Create a data URL
-                    image_data_url = f"data:image/png;base64,{image_base64}"
-
+                    random_id = random.randint(1000, 9999)
+                    file_extension = ".png"
+                    filename = f"image_{random_id}{file_extension}"
+                    with open(filename, "wb") as file:
+                        file.write(get_image.content)
+                    print(f"Saved image as: {filename}")
             elif platform == "web":
                 print("WEB")
                 # Handle web case if needed
             else:
                 pass
 
-            if image_data_url is not None:
-                # Load the image from image_data_url
-                image_data = base64.b64decode(image_base64)
-                nparr = np.frombuffer(image_data, np.uint8)
-                image = cv.imdecode(nparr, 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
+            image = cv.imread("https://tommy24-classifier.hf.space/file=/tmp/",filename)
+            image = cv.resize(image, (224, 224))
+            image_array = np.asarray(image)
+            image_data = cv.resize(imageData, (224, 224))
+            normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+            data[0] = normalized_image_array
 
-                prediction = model.predict(data)
+            prediction = model.predict(data)
 
-                max_label_index = None
-                max_prediction_value = -1
+            max_label_index = None
+            max_prediction_value = -1
 
-                print('Prediction')
+            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})
+            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}')
+            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 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_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})
+                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}"
-                        }
+                    headers = {
+                        "Content-Type": "application/json",
+                        "Authorization": f"Bearer {auth}"
+                    }
 
-                        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": "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": 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": "Data URL", "data_url": image_data_url})
             return output
+
         else:
             output = []
 
@@ -636,7 +467,7 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
 user_inputs = [
     gr.Textbox(label="Platform", type="text"),
     gr.Textbox(label="User Input", type="text"),
-    gr.Textbox(label="Images", type="text"),
+    gr.Textbox(label="Image", type="text"),
     gr.Textbox(label="Textbox2", type="text"),
     gr.Textbox(label="Textbox3", type="password")
 ]
@@ -648,3 +479,170 @@ iface = gr.Interface(
     title="Classifier",
 )
 iface.launch()
+
+# import gradio as gr
+# import numpy as np
+# import cv2 as cv
+# import requests
+# import random
+# import os
+# import tensorflow as tf
+# import base64
+
+# 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)
+
+# # 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()
+
+# messages = [{"role": "system", "content": system}]
+
+# def classify(platform, UserInput, Images, Textbox2, Textbox3):
+#     if Textbox3 == code:
+#         imageData = None
+#         image_data_url = None  # Initialize image_data_url
+#         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:
+#                     # Convert the image data to base64
+#                     image_base64 = base64.b64encode(get_image.content).decode("utf-8")
+
+#                     # Create a data URL
+#                     image_data_url = f"data:image/png;base64,{image_base64}"
+
+#             elif platform == "web":
+#                 print("WEB")
+#                 # Handle web case if needed
+#             else:
+#                 pass
+
+#             if image_data_url is not None:
+#                 # Load the image from image_data_url
+#                 image_data = base64.b64decode(image_base64)
+#                 nparr = np.frombuffer(image_data, np.uint8)
+#                 image = cv.imdecode(nparr, 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()
+
+#                         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": "Data URL", "data_url": image_data_url})
+#             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="Images", 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()