Upload app.py

app.py

@@ -0,0 +1,71 @@
+from langchain.chains import LLMChain
+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):\
+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"
+
+# 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")
+
+if st.sidebar.button("Get response"):
+    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.❤")
+    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.")