chatbot updates

app.py

@@ -56,8 +56,8 @@ print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
 def load_models():
     #OpenAI elements
     #secrets = toml.load(".vscode/streamlit/secrets.toml")
-    #client_d = OpenAI(api_key = secrets["OPENAI_API_KEY"])
     client_d = OpenAI(api_key = st.secrets["OPENAI_API_KEY"])
+    #client_chat = OpenAI(api_key = st.secrets["OPENAI_API_KEY"])
 
     module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"
     detector_d = hub.load(module_handle).signatures['default'];
@@ -350,6 +350,17 @@ def draw_boxes(image, boxes, class_names, scores, max_boxes=3, min_score=0.1):
 # ----------------------------------------------------------------------------------------------------//
 # Streamlit app
 
+st.title("Image-based plant Disease Identification")
+
+if 'plant_detection_count' not in st.session_state:
+    st.session_state['plant_detection_count'] = 0
+
+if 'plant_detection_count' not in st.session_state:
+    st.session_state['plant_disease_class_value'] = ""
+
+if 'chat_mode_on' not in st.session_state:
+    st.session_state['chat_mode_on'] = 0
+    
 
 def openai_remedy(searchval):
         completion = client.chat.completions.create(
@@ -365,7 +376,23 @@ def openai_remedy(searchval):
         #st.markdown(completion.choices[0].delta.content)
         return
 
-st.title("Image-based plant Disease Identification")
+def chat_help(plant_disease_class):
+    if "messages" not in st.session_state:
+        st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you further with " + plant_disease_class + "?"}]
+
+    for msg in st.session_state.messages:
+        st.chat_message(msg["role"]).write(msg["content"])
+
+    if prompt := st.chat_input():
+        st.session_state.messages.append({"role": "system", "content": "Limit responses to " + plant_disease_class})
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.chat_message("user").write(prompt)
+        response = client_d.chat.completions.create(model="gpt-4-turbo", messages=st.session_state.messages)
+        msg = response.choices[0].message.content
+        st.session_state.messages.append({"role": "assistant", "content": msg})
+        st.chat_message("assistant").write(msg)
+
+
 
 tab1, tab2, tab3 = st.tabs(["Home", "Solution", "Team"])
 
@@ -389,20 +416,30 @@ Early detection of plant diseases is paramount for farmers to protect their crop
 
     st.write("With more than 50% of the population in India still relying on agriculture and with the average farm sizes and incomes being very small, we believe that cost effective solutions for early detection and treatment solutions for disease could significantly improve the quality of produce and lives of farmers. With smartphones being ubiquitous, we believe providing solutions to farmers over a smartphone is the most penetrative form.")
 
+    st.write("""
+### Training Dataset
 
+Publicly available PlantVillage dataset was used. It consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease.
+""")
 
 
+    
 #Second Tab: Image upload and disease detection and remidy susgestions
 with tab2: 
     # Load and display the image
-    uploaded_file = st.file_uploader("Upload Leaf Image...", type=["jpg", "jpeg", "png"], key="uploader")
-
-    if uploaded_file is not None:
-        print("Image successfully uploaded!")
-        # Read the uploaded image file
-        #st.image(uploaded_file, caption='Uploaded Image', use_column_width=True,width=100)
-        with st.columns(3)[0]:
-            st.image(uploaded_file, caption='Image uploaded! Trying to detect objects in it...', width=300)
+    #uploaded_file = st.file_uploader("Upload Leaf Image...", type=["jpg", "jpeg", "png"], key="uploader")
+    
+    with st.form("my-form", clear_on_submit=True):
+        uploaded_file = st.file_uploader("Upload Leaf Image...", type=["jpg", "jpeg", "png"], key="uploader")
+        submitted = st.form_submit_button("UPLOAD!")
+
+    if uploaded_file is not None and st.session_state['chat_mode_on'] == 0:
+        #st.markdown("Image successfully uploaded!")
+        st.session_state['plant_detection_count'] = 0
+        st.session_state['plant_disease_class_value'] = ""
+       
+        #with st.columns(3)[0]:
+        st.image(uploaded_file, caption='Image uploaded! Trying to detect objects in it...', width=300)
         image = Image.open(uploaded_file)
 
         image_for_drawing = image.copy()
@@ -436,7 +473,8 @@ with tab2:
             entity = detection_class_entities[idx].decode('utf-8')
 
             if "Plant" == entity:
-                plant_detection_count = 1;
+                plant_detection_count_p = 1
+                st.session_state['plant_detection_count'] = 1
                 plant_score = detection_scores[idx]
                 st.write(f"Plant Probability score using Faster R-CNN Inception Resnet V2 Object detection model :  {plant_score:.2%}")
                 result1 = classify_image(image)
@@ -448,13 +486,20 @@ with tab2:
                     newresult2 = newresult.replace("-"," ")
                     st.markdown("Fetching disease management steps for " + ":red[" + newresult2 + "]... :eyes:")
                     openai_remedy(newresult2)
+                    st.session_state['plant_disease_class_value'] = newresult2
+                    uploaded_file = None
 
         if plant_detection_count == 0:
             st.markdown("This is not a plant / leaf image")
                     
     else:
         print("No file uploaded.")
-    
+
+    if st.session_state['plant_detection_count'] == 1 and st.session_state['plant_disease_class_value'] != "" :
+        st.session_state['chat_mode_on'] = 1
+        chat_help(st.session_state['plant_disease_class_value'])
+
+        
     # Disclaimer
     st.write("""
 ### Disclaimer