Update app.py

app.py

@@ -26,8 +26,13 @@
 # with open("labels.txt", "r") as file:
 #     labels = file.read().splitlines()
 
-# def classify(Textbox, Image, Textbox2, Textbox3):
+# messages = [
+#     {"role": "system", "content": system}
+# ]
+
+# def classify(UserInput, Image, Textbox2, Textbox3):
 #     if Textbox3 == code:
+#         print("Image:  ", Image)
 #         if Image is not None:
 #             output = []
 #             image_data = np.array(Image)
@@ -50,17 +55,8 @@
 #             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)
+#             Textbox2_edited.append(UserInput)
+#             messages.append({"role": "user", "content": UserInput})
         
 #             for i, label in enumerate(labels):
 #                 prediction_value = float(prediction[0][i])
@@ -73,27 +69,30 @@
         
 #             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})
+#                 max_rounded_prediction = round(max_prediction_value, 2)
+#                 print(f'Maximum Prediction: {max_label} with a value of {max_rounded_prediction}')
     
-#                 headers = {
-#                     "Content-Type": "application/json",
-#                     "Authorization": f"Bearer {auth}"
-#                 }
+#                 time.sleep(1)
+#                 if max_rounded_prediction > 0.5:
+#                     print("\nWays to dispose of this waste: " + max_label)
+#                     messages.append({"role": "user", "content": content + " " + max_label})
         
-#                 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})
+#                     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
 
@@ -104,20 +103,17 @@
 #             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)
-            
+#             Textbox2_edited.append(UserInput)
+        
 #             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 +121,20 @@
 #             }
     
 #             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(),
@@ -169,6 +165,7 @@ 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
 model = None
 image = None
@@ -187,11 +184,19 @@ messages = [
     {"role": "system", "content": system}
 ]
 
-def classify(UserInput, Image, Textbox2, Textbox3):
+def classify(platform,UserInput, Image, Textbox2, Textbox3):
     if Textbox3 == code:
         print("Image:  ", Image)
         if Image is not None:
             output = []
+            headers = {
+                "Authorization": f"Bearer {auth2}"
+            }
+            if platform == "wh":
+                get_image = requests.get(image, headers=headers)
+                print(get_image.content)
+            elif platform == "web":
+                print("WEB")
             image_data = np.array(Image)
             image_data = cv.resize(image_data, (224, 224))
             image_array = np.asarray(image_data)
@@ -293,8 +298,9 @@ def classify(UserInput, Image, Textbox2, Textbox3):
         return "Unauthorized"
 
 user_inputs = [
+    gr.Textbox(label="Platform", type="text"),
     gr.Textbox(label="User Input", type="text"),
-    gr.Image(),
+    gr.Textbox(label="Image", type="text"),
     gr.Textbox(label="Textbox2", type="text"),
     gr.Textbox(label="Textbox3", type="password")
 ]