change

app.py

@@ -3,15 +3,12 @@ from langchain_community.llms import OpenAI
 from langchain_core.prompts import PromptTemplate
 import streamlit as st
 
-# Set the page to wide mode
-st.set_page_config(layout="wide")
-
 mini_template = "You are an expert researcher. You\'ve talked to hundreds of {Target Audience}. \
 Each person in the niche of {Target Audience} has certain struggles that make it easier to sell {My Course}. \
 These are called Pain Points. There's a recipe for getting to the core of the Pain Points of {Target Audience}. \
 Namely, answer each of these Questions 3 times, each getting deeper in the issues of {Target Audience}, \
 appealing to their Emotions and uncertainties related to {My Course}. \
-The Questions (answer each QUESTION 3 times in listicle format according to the instructions):\
+The Questions (answer each QUESTION 3 tiems in listicle format according to the instructions):\
 1. What keeps them awake at night?\
 2. What are they afraid of?\
 3. What are they angry about?\
@@ -30,42 +27,22 @@ chain = LLMChain(llm=OpenAI(), prompt=prompt)
 #target_audience = "professionals looking for course on Power BI"
 #my_course = "Zero to Hero in PowerBI"
 
-# Use the sidebar for input
-target_audience = st.sidebar.text_input("Enter your target audience")
-my_course = st.sidebar.text_input("Enter your course name")
+target_audience = st.text_input("Enter your target audience")
+my_course = st.text_input("Enter your course name")
 
-if st.sidebar.button("Get response"):
+if st.button("Get resposne"):
     if target_audience and my_course:
         with st.spinner("Generating response..."):
             with st.expander("Show prompt", expanded=False):
-                st.info(prompt.template)
-            answer = chain.run({"Target Audience": target_audience, "My Course": my_course})
-
-        # Split the 'answer' into sections based on the questions
-        sections = [section.strip() for section in answer.split("\n\n") if section.strip() != ""]
-
-        # Assuming there are exactly three sections based on your output structure
-        if len(sections) == 3:
-            # Extract titles for tabs
-            titles = [section.split('\n')[0] for section in sections]
-            
-            # Extract content for each section, removing the title
-            contents = [section.split('\n')[1:] for section in sections]
-
-            # Create tabs for each category
-            tabs = st.tabs(titles)
-
-            for i, tab in enumerate(tabs):
-                with tab:
-                    st.header(titles[i])
-                    for content in contents[i]:
-                        st.markdown(content)
-        else:
-            st.error("The answer format does not match the expected structure.")
-        st.success("Hope you like the response.❤")
+                    st.info(prompt.template)
+            answer = chain.run({"Target Audience": target_audience, "My Course":my_course})
+    
+        st.write(answer)
+        st.success("Hope you like the resposne.❤")
     elif target_audience:
         st.error("Enter your course name.")
     elif my_course:
         st.error("Enter your target audience.")
+
     else:
         st.error("No input detected, Please provide the desired information.")

app0.py

@@ -1,48 +0,0 @@
-from langchain.chains import LLMChain
-from langchain_community.llms import OpenAI
-from langchain_core.prompts import PromptTemplate
-import streamlit as st
-
-mini_template = "You are an expert researcher. You\'ve talked to hundreds of {Target Audience}. \
-Each person in the niche of {Target Audience} has certain struggles that make it easier to sell {My Course}. \
-These are called Pain Points. There's a recipe for getting to the core of the Pain Points of {Target Audience}. \
-Namely, answer each of these Questions 3 times, each getting deeper in the issues of {Target Audience}, \
-appealing to their Emotions and uncertainties related to {My Course}. \
-The Questions (answer each QUESTION 3 tiems in listicle format according to the instructions):\
-1. What keeps them awake at night?\
-2. What are they afraid of?\
-3. What are they angry about?\
-"
-
-
-st.title("Saas Application")
-
-prompt = PromptTemplate(
-    input_variables = ["Target Audience", "My Course"],
-    template=mini_template,
-)
-
-chain = LLMChain(llm=OpenAI(), prompt=prompt)
-
-#target_audience = "professionals looking for course on Power BI"
-#my_course = "Zero to Hero in PowerBI"
-
-target_audience = st.text_input("Enter your target audience")
-my_course = st.text_input("Enter your course name")
-
-if st.button("Get resposne"):
-    if target_audience and my_course:
-        with st.spinner("Generating response..."):
-            with st.expander("Show prompt", expanded=False):
-                    st.info(prompt.template)
-            answer = chain.run({"Target Audience": target_audience, "My Course":my_course})
-    
-        st.write(answer)
-        st.success("Hope you like the resposne.❤")
-    elif target_audience:
-        st.error("Enter your course name.")
-    elif my_course:
-        st.error("Enter your target audience.")
-
-    else:
-        st.error("No input detected, Please provide the desired information.")