Update app.py

app.py

@@ -28,54 +28,95 @@ with open("labels.txt", "r") as file:
 
 def classify(Textbox, Image, Textbox2, Textbox3):
     if Textbox3 == code:
-        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)
+        if Image != 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
         
-        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)
+            import tensorflow as tf
+            model = tf.keras.models.load_model('keras_model.h5')
         
-        for i in Textbox2_edited:
-            messages.append(
-                {"role": "user", "content": i}
-            )
-        print("messages after appending:", messages)
+            prediction = model.predict(data)
+            
+            max_label_index = None
+            max_prediction_value = -1
     
-        for i, label in enumerate(labels):
-            prediction_value = float(prediction[0][i])
-            rounded_value = round(prediction_value, 2)
-            print(f'{label}: {rounded_value}')
+            print('Prediction')
     
-            if prediction_value > max_prediction_value:
-                max_label_index = i
-                max_prediction_value = prediction_value 
+            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})
     
-        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)}')
+                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)
-            print("\nWays to dispose of this waste: " + max_label)
             messages.append({"role": "user", "content": content + " " + max_label})
             messages.append({"role": "user", "content": Textbox})
 
@@ -92,14 +133,15 @@ def classify(Textbox, Image, Textbox2, Textbox3):
             reply = response["choices"][0]["message"]["content"]
             messages.append({"role": "assistant", "content": reply})
 
-            output.append({"type": max_label, "prediction_value": rounded_value, "content": reply})
-        
-        return output
+            output.append({"Mode":"Chat","content": reply})
+            
+            return output
+
     else:
         return "Unauthorized"
-
+        
 user_inputs = [
-    gr.Textbox(label="Textbox", type="text"),
+    gr.Textbox(label="User Input", type="text"),
     gr.Image(),
     gr.Textbox(label="Textbox2", type="text"),
     gr.Textbox(label="Textbox3", type="password")