Adding both checkbox

app.py

@@ -62,7 +62,7 @@ def consolidate_score(thermal_result,rgb_result):
   return output_consol
 thermal_check = st.checkbox("Thermal Input")
 rgb_check  = st.checkbox("RGB Input")
-thermal_execution,rgb_execution=False,False
+both_check  = st.checkbox("Both")
 if(thermal_check or rgb_check):
     if(thermal_check):
     
@@ -82,35 +82,37 @@ if(thermal_check or rgb_check):
             pipe_thermal = create_pipeline(feature_extractor_thermal,model_thermal)
             # opencv_image = np.array(opencv_image)
             thermal_result=pipe_thermal(input_image)
-            thermal_execution=True
+        
+        
+        consolidated_result=get_output_label(thermal_result)
+        if(consolidated_result!="RiceLeafs_Healthy"):
+            f = open("remedy.json")
+            data = json.load(f)
+            for i in data[consolidated_result.split("_")[0]]:
+                if(i["disease_name"]==consolidated_result.split("_")[1]):
+                    for key in i:
+                        st.write(key," : ",i[key])
+        else:
+            st.write(consolidated_result.split("_")[0]," is ",consolidated_result.split("_")[1])
+        
     if(rgb_check):
             
-            st.write("RGB Image file uploader...")
-            uploaded_file = st.file_uploader("Choose a rgb image file", type=["jpg","png"])
+        st.write("RGB Image file uploader...")
+        uploaded_file = st.file_uploader("Choose a rgb image file", type=["jpg","png"])
+
+        if uploaded_file is not None:
+            input_image = Image.open(uploaded_file)
+            st.write("Input Image...")
+            st.image(input_image)
+            dataset_thermal,dataset_rgb = load_dataset_from_Hugging_Face()
+            labels=dataset_thermal["train"].features["label"].names
+            id2label={k:v for k,v in enumerate(labels)}
+            label2id = {v:k for k,v in enumerate(labels)}
+            feature_extractor_rgb,model_rgb = load_model_from_Hugging_Face("rgb")
+            pipe_rgb = create_pipeline(feature_extractor_rgb,model_rgb)
+            rgb_result=pipe_rgb(input_image)
 
-            if uploaded_file is not None:
-                input_image = Image.open(uploaded_file)
-                st.write("Input Image...")
-                st.image(input_image)
-                dataset_thermal,dataset_rgb = load_dataset_from_Hugging_Face()
-                labels=dataset_thermal["train"].features["label"].names
-                id2label={k:v for k,v in enumerate(labels)}
-                label2id = {v:k for k,v in enumerate(labels)}
-                feature_extractor_rgb,model_rgb = load_model_from_Hugging_Face("rgb")
-                pipe_rgb = create_pipeline(feature_extractor_rgb,model_rgb)
-                rgb_result=pipe_rgb(input_image)
-                rgb_execution=True
-    if(not rgb_check):
-       rgb_execution=True
-    if(not thermal_check):
-       thermal_execution=True
-    if(thermal_check and rgb_check and thermal_execution and rgb_execution):
-        consolidated_result=get_output_label(consolidate_score(thermal_result,rgb_result))
-    elif(thermal_check and thermal_execution and not rgb_check):
-        consolidated_result=get_output_label(thermal_result)
-    elif(rgb_check and rgb_execution and not thermal_check):
         consolidated_result=get_output_label(rgb_result)
-    if(thermal_execution and rgb_execution):
         if(consolidated_result!="RiceLeafs_Healthy"):
             f = open("remedy.json")
             data = json.load(f)
@@ -119,4 +121,46 @@ if(thermal_check or rgb_check):
                     for key in i:
                         st.write(key," : ",i[key])
         else:
-            st.write(consolidated_result.split("_")[0]," is ",consolidated_result.split("_")[1])
+            st.write(consolidated_result.split("_")[0]," is ",consolidated_result.split("_")[1])
+
+if(both_check):
+    st.write("Thermal Image file uploader...")
+    uploaded_file = st.file_uploader("Choose a thermal image file", type=["jpg","png"])
+
+    if uploaded_file is not None:
+        input_image = Image.open(uploaded_file)
+        st.write("Input Image...")
+        st.image(input_image)
+        dataset_thermal,dataset_rgb = load_dataset_from_Hugging_Face()
+        labels=dataset_thermal["train"].features["label"].names
+        id2label={k:v for k,v in enumerate(labels)}
+        label2id = {v:k for k,v in enumerate(labels)}
+
+        feature_extractor_thermal,model_thermal = load_model_from_Hugging_Face("thermal")
+        pipe_thermal = create_pipeline(feature_extractor_thermal,model_thermal)
+        # opencv_image = np.array(opencv_image)
+        thermal_result=pipe_thermal(input_image)
+    st.write("RGB Image file uploader...")
+    uploaded_file = st.file_uploader("Choose a rgb image file", type=["jpg","png"])
+
+    if uploaded_file is not None:
+        input_image = Image.open(uploaded_file)
+        st.write("Input Image...")
+        st.image(input_image)
+        dataset_thermal,dataset_rgb = load_dataset_from_Hugging_Face()
+        labels=dataset_thermal["train"].features["label"].names
+        id2label={k:v for k,v in enumerate(labels)}
+        label2id = {v:k for k,v in enumerate(labels)}
+        feature_extractor_rgb,model_rgb = load_model_from_Hugging_Face("rgb")
+        pipe_rgb = create_pipeline(feature_extractor_rgb,model_rgb)
+        rgb_result=pipe_rgb(input_image)
+    consolidated_result=get_output_label(consolidate_score(thermal_result,rgb_result))
+    if(consolidated_result!="RiceLeafs_Healthy"):
+            f = open("remedy.json")
+            data = json.load(f)
+            for i in data[consolidated_result.split("_")[0]]:
+                if(i["disease_name"]==consolidated_result.split("_")[1]):
+                    for key in i:
+                        st.write(key," : ",i[key])
+    else:
+        st.write(consolidated_result.split("_")[0]," is ",consolidated_result.split("_")[1])