Update app.py

app.py

@@ -24,16 +24,15 @@ data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 with open("labels.txt", "r") as file:
     labels = file.read().splitlines()
 
-def classify(image_path, text_input):
-    if text_input == code:
-        output = []  # Create an empty list for output
+def classify(image_path, Textbox2, Textbox3):
+    if Textbox3 == code:
+        output = []
         image_data = np.array(image_path)
         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
     
-        # Load the model within the classify function
         import tensorflow as tf
         model = tf.keras.models.load_model('keras_model.h5')
     
@@ -43,6 +42,20 @@ def classify(image_path, text_input):
         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": content},
+        ]
+        for i in Textbox2_edited:
+            messages.append(
+                {"role": "user", "content": i}
+            )
     
         for i, label in enumerate(labels):
             prediction_value = float(prediction[0][i])
@@ -51,7 +64,7 @@ def classify(image_path, text_input):
     
             if prediction_value > max_prediction_value:
                 max_label_index = i
-                max_prediction_value = prediction_value  # Update max_prediction_value
+                max_prediction_value = prediction_value 
     
         if max_label_index is not None:
             max_label = labels[max_label_index].split(' ', 1)[1]
@@ -59,10 +72,9 @@ def classify(image_path, text_input):
     
             time.sleep(1)
             print("\nWays to dispose of this waste: " + max_label)
-            payload = [
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": "Give me the steps to dispose of this waste in bullet points (5 max): " + max_label}
-            ]
+            messages.append(
+                {"role": "user", "content": Textbox},
+            )
     
             response = requests.post(host, json={
                 "messages": payload,
@@ -74,17 +86,23 @@ def classify(image_path, text_input):
             }).json()
 
             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  # Return the populated output list
+        return output
     else:
         return "Unauthorized"
 
 iface = gr.Interface(
     fn=classify,
-    inputs=[gr.inputs.Image(), "text"],
-    outputs=gr.outputs.JSON(),  # Output as JSON
+    inputs = [
+        gr.inputs.Image(),
+        gr.inputs.Textbox(label="Textbox2",type="text"),
+        gr.inputs.Textbox(label="Textbox3",type="password")
+    ]
+
+    outputs=gr.outputs.JSON(),
     title="Waste Classifier",
     description="Upload an image to classify and get disposal instructions."
 )