Update app.py

app.py

@@ -26,7 +26,42 @@ embeddings = HuggingFaceEmbeddings(
 db = FAISS.load_local("IPCC_index_e5_1000_all", embeddings)
 
 
-
+question1 = 'Why does temperature increase?'
+question2 = 'What evidence we have of climate change?'
+question3 = 'What is the link between health and climate change?'
+
+def click_button(button_text):
+    if prompt := button_text:
+
+    #if prompt := st.text_input(label="Your quesiton:",value=st.session_state.button_text if 'button_text' in st.session_state else 'Text your question'):
+        if not openai_api_key:
+            st.info("Please add your OpenAI API key to continue.")
+            st.stop()
+
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.chat_message("user").write(prompt)
+        with st.spinner("Thinking..."):
+            result = generate_response(prompt)
+            result_r = result["result"]
+            index = result_r.find("Highlight:")
+
+
+
+            # Display assistant response in chat message container
+            with st.chat_message("assistant"):
+                message_placeholder = st.empty()
+                full_response = ""
+                assistant_response = result_r
+                # Simulate stream of response with milliseconds delay
+                for chunk in assistant_response.split():
+                    full_response += chunk + " "
+                    time.sleep(0.05)
+                    # Add a blinking cursor to simulate typing
+                    message_placeholder.write(full_response + "▌")
+                message_placeholder.write(result_r)
+            # Add assistant response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": result_r})
+        
 def generate_response(input_text):
     docs = db.similarity_search(input_text,k=5)
 
@@ -44,16 +79,14 @@ def generate_response(input_text):
 
     {question}
 
-    Generate a comprehensive and informative answer to the general audience of 100 words or less for the \
+    Generate a comprehensive and informative answer and three next questions to the general audience of 100 words or less for the \
     given question based solely on the provided search results (hyperlink and source). You must \
     only use information from the provided search results. Use an unbiased and \
     journalistic tone. Combine search results together into a coherent answer. Do not \
     repeat text. Only use \
     relevant results that answer the question accurately.  list these sources at the end of your answer  \
-    in a section named "source". 
-    
-    After the "source" section, add a "highlight" section\
-    And add a short highlight  of your answer in humor and make sure no more than 5 words.\
+    in a section named "source". After the "source" section, makre sure provide three next questions in the section of predicted \
+\
     
     Format your answer in markdown format
 
@@ -96,7 +129,7 @@ def generate_response(input_text):
     llm = ChatOpenAI(
         model_name="gpt-3.5-turbo-16k",
         temperature=0.05,
-        max_tokens=2000, 
+        max_tokens=2500, 
         openai_api_key=openai_api_key
     )
 
@@ -126,55 +159,59 @@ st.caption("💬 A Climate Change chatbot powered by OpenAI LLM and IPCC documen
 
 
 if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "Any question about the climate change?"}]
+    st.session_state["messages"] = [{"role": "assistant", "content": "Any question about the climate change? Here are some examples:"}]
 
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
 
+
+
+
 if prompt := st.chat_input():
+
+#if prompt := st.text_input(label="Your quesiton:",value=st.session_state.button_text if 'button_text' in st.session_state else 'Text your question'):
     if not openai_api_key:
         st.info("Please add your OpenAI API key to continue.")
         st.stop()
 
     st.session_state.messages.append({"role": "user", "content": prompt})
     st.chat_message("user").write(prompt)
-    result = generate_response(prompt)
-    result_r = result["result"]
-    index = result_r.find("Highlight:")
-
-    # Extract everything after "Highlight:"
-    match = re.search(r"Highlight: (.+)", result_r)
-    if match:
-        highlighted_text = match.group(1)
-    else:
-        highlighted_text="hello world"
-
-
-
+ 
 
-        # Display assistant response in chat message container
     with st.chat_message("assistant"):
+        with st.spinner("thinking..."):
+            result = generate_response(prompt)
+            result_r = result["result"]
+            index = result_r.find("Highlight:")
         message_placeholder = st.empty()
         full_response = ""
         assistant_response = result_r
-        # Simulate stream of response with milliseconds delay
+            # Simulate stream of response with milliseconds delay
         for chunk in assistant_response.split():
-            full_response += chunk + " "
-            time.sleep(0.05)
-            # Add a blinking cursor to simulate typing
-            message_placeholder.write(full_response + "▌")
+                full_response += chunk + " "
+                time.sleep(0.05)
+                # Add a blinking cursor to simulate typing
+                message_placeholder.write(full_response + "▌")
         message_placeholder.write(result_r)
-    # Add assistant response to chat history
+
+        if re.search(r'Next Questions?:', result_r, flags=re.IGNORECASE):
+
+            questions_text = re.split(r'Next Questions?:', result_r, flags=re.IGNORECASE)[-1]
+
+            pattern = r'\d+\.\s*([^?]+\?)'
+
+            # Use re.findall to find all matches of the pattern in the questions_text
+            matches = re.findall(pattern, questions_text)
+
+            question1 = matches[0].strip()
+            question2 = matches[1].strip()
+            question3 = matches[2].strip()
+        # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": result_r})
         
-    #st.session_state.messages.append({"role": "assistant", "content": result["result"]})
-    #st.chat_message("assistant").write(result_r)
-    #display_typing_effect(st.chat_message("assistant"), result_r)
-    #st.markdown(result['source_documents'][0])
-    #st.markdown(result['source_documents'][1])
-    #st.markdown(result['source_documents'][2])
-    #st.markdown(result['source_documents'][3])
-    #st.markdown(result['source_documents'][4])
 
 
-    #st.image("https://cataas.com/cat/says/"+highlighted_text)
+button_col1, button_col2, button_col3= st.columns([5, 5, 5])
+st.code(question1)
+st.code(question2)
+st.code(question3)