Update app.py

app.py

@@ -1,3 +1,160 @@
+# import gradio as gr
+# import numpy as np
+# import cv2 as cv
+# import requests
+# import time
+# import os
+
+# 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")
+# data = None
+# model = None
+# image = None
+# prediction = None
+# labels = None
+
+# print('START')
+# np.set_printoptions(suppress=True)
+
+# data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+# with open("labels.txt", "r") as file:
+#     labels = file.read().splitlines()
+
+# def classify(Textbox, Image, Textbox2, Textbox3):
+#     if Textbox3 == code:
+#         if Image is not None:
+#             output = []
+#             image_data = np.array(Image)
+#             image_data = cv.resize(image_data, (224, 224))
+#             image_array = np.asarray(image_data)
+#             normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+#             data[0] = normalized_image_array
+        
+#             import tensorflow as tf
+#             model = tf.keras.models.load_model('keras_model.h5')
+        
+#             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(Textbox)
+#             messages = [
+#                 {"role": "system", "content": system},
+#             ]
+#             print("Messages",messages)
+            
+#             # for i in Textbox2_edited:
+#             #     messages.append(
+#             #         {"role": "user", "content": i}
+#             #     )
+#             print("messages after appending:", messages)
+        
+#             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]
+#                 print(f'Maximum Prediction: {max_label} with a value of {round(max_prediction_value, 2)}')
+        
+#                 time.sleep(1)
+#                 print("\nWays to dispose of this waste: " + max_label)
+#                 messages.append({"role": "user", "content": Textbox})
+#                 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": rounded_value, "content": reply})
+            
+#             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(Textbox)
+#             messages = [
+#                 {"role": "system", "content": system},
+#             ]
+#             print("Messages",messages)
+            
+#             for i in Textbox2_edited:
+#                 messages.append(
+#                     {"role": "user", "content": i}
+#                 )
+#             print("messages after appending:", messages)
+        
+#             time.sleep(1)
+#             messages.append({"role": "user", "content": Textbox})
+
+#             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="User Input", type="text"),
+#     gr.Image(),
+#     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
@@ -26,7 +183,7 @@ data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 with open("labels.txt", "r") as file:
     labels = file.read().splitlines()
 
-def classify(Textbox, Image, Textbox2, Textbox3):
+def classify(UserInput, Image, Textbox2, Textbox3):
     if Textbox3 == code:
         if Image is not None:
             output = []
@@ -50,17 +207,11 @@ def classify(Textbox, Image, Textbox2, Textbox3):
             Textbox2 = Textbox2.split(",")
             Textbox2_edited = [x.strip() for x in Textbox2]
             Textbox2_edited = list(Textbox2_edited)
-            Textbox2_edited.append(Textbox)
+            Textbox2_edited.append(UserInput)
             messages = [
                 {"role": "system", "content": system},
+                {"role": "user", "content": UserInput},
             ]
-            print("Messages",messages)
-            
-            # for i in Textbox2_edited:
-            #     messages.append(
-            #         {"role": "user", "content": i}
-            #     )
-            print("messages after appending:", messages)
         
             for i, label in enumerate(labels):
                 prediction_value = float(prediction[0][i])
@@ -74,10 +225,9 @@ def classify(Textbox, Image, Textbox2, Textbox3):
             if max_label_index is not None:
                 max_label = labels[max_label_index].split(' ', 1)[1]
                 print(f'Maximum Prediction: {max_label} with a value of {round(max_prediction_value, 2)}')
-        
+    
                 time.sleep(1)
                 print("\nWays to dispose of this waste: " + max_label)
-                messages.append({"role": "user", "content": Textbox})
                 messages.append({"role": "user", "content": content + " " + max_label})
     
                 headers = {
@@ -86,14 +236,14 @@ def classify(Textbox, Image, Textbox2, Textbox3):
                 }
         
                 response = requests.post(host, headers=headers, json={
-                    "messages":messages,
-                    "model":model_llm
+                    "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": rounded_value, "content": reply})
+                output.append({"Mode": "Image", "type": max_label, "prediction_value": rounded_value, "content": reply})
             
             return output
 
@@ -104,20 +254,18 @@ def classify(Textbox, Image, Textbox2, Textbox3):
             Textbox2 = Textbox2.split(",")
             Textbox2_edited = [x.strip() for x in Textbox2]
             Textbox2_edited = list(Textbox2_edited)
-            Textbox2_edited.append(Textbox)
+            Textbox2_edited.append(UserInput)
             messages = [
                 {"role": "system", "content": system},
             ]
-            print("Messages",messages)
-            
+        
             for i in Textbox2_edited:
                 messages.append(
                     {"role": "user", "content": i}
                 )
-            print("messages after appending:", messages)
         
             time.sleep(1)
-            messages.append({"role": "user", "content": Textbox})
+            messages.append({"role": "user", "content": UserInput})
 
             headers = {
                 "Content-Type": "application/json",
@@ -125,20 +273,20 @@ def classify(Textbox, Image, Textbox2, Textbox3):
             }
     
             response = requests.post(host, headers=headers, json={
-                "messages":messages,
-                "model":model_llm
+                "messages": messages,
+                "model": model_llm
             }).json()
             
             reply = response["choices"][0]["message"]["content"]
             messages.append({"role": "assistant", "content": reply})
 
-            output.append({"Mode":"Chat","content": reply})
+            output.append({"Mode": "Chat", "content": reply})
             
             return output
 
     else:
         return "Unauthorized"
-        
+
 user_inputs = [
     gr.Textbox(label="User Input", type="text"),
     gr.Image(),