Update app.py

app.py

@@ -315,15 +315,183 @@
 # 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 io
-from PIL import Image
+import random
 import os
 import tensorflow as tf
-import random
+import base64
 
 host = os.environ.get("host")
 code = os.environ.get("code")
@@ -333,7 +501,6 @@ 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)
 
@@ -344,9 +511,15 @@ 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 save_image_as_base64(image_content, file_extension=".png"):
+    # Encode the image content as base64
+    image_base64 = base64.b64encode(image_content).decode("utf-8")
+
+    # Construct the data URL
+    data_url = f"data:image/{file_extension};base64,{image_base64}"
+    return data_url
 
 def classify(platform, UserInput, Images, Textbox2, Textbox3):
     if Textbox3 == code:
@@ -359,6 +532,7 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             if platform == "wh":
                 get_image = requests.get(Images, headers=headers)
                 if get_image.status_code == 200:
+                    # Generate a random ID for the file
                     random_id = random.randint(1000, 9999)
                     file_extension = ".png"
                     filename = f"image_{random_id}{file_extension}"
@@ -368,70 +542,74 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
 
                     full_path = os.path.join(os.getcwd(), filename)
                     print(f"Saved image as: {full_path}")
+
+                    # Convert the image content to base64
+                    image_data_url = save_image_as_base64(get_image.content, file_extension)
+                    print(f"Data URL of the image: {image_data_url}")
             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
+            if imageData is not None:
+                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)
+                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"})
 
             return output
-
         else:
             output = []
 
@@ -449,7 +627,7 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             messages.append({"role": "user", "content": UserInput})
 
             headers = {
-                "Content-Type": "application/json",
+                "Content-Type": "application.json",
                 "Authorization": f"Bearer {auth}"
             }