Update app.py

app.py

@@ -12,8 +12,6 @@ from langchain import HuggingFaceHub, LLMChain, PromptTemplate
 from langchain.memory import ConversationBufferWindowMemory
 from langchain.chains import ConversationalRetrievalChain
 
-os.environ["OPENAI_API_KEY"] ='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'
-
 trait_content_df=pd.read_csv('AI Personality Chart trait_content.csv')
 trait_content_df=trait_content_df.drop(0,axis=0)
 trait_content_df.rename(columns={'Column 1':'Question','Column 2':'Options','Column 3':'Traits','Column 4':'Content'},inplace=True)
@@ -21,7 +19,7 @@ trait_content_df['Title'].fillna(method='ffill',inplace=True)
 trait_content_df['Question'].fillna(method='ffill',inplace=True)
 
 template = """
-    Imagine you are a person that is looking for making a cool dating app bio. Craft distinctive responses for each user based on traits extracted and refer from the content provided for each trait, even if the prompts are similar.Make a single Bio for all traits at once, no need to make different for each trait. Respond it in 2nd person view. Avoid using the same sentences for different users while keeping the response within 100 words.
+   Imagine you are a person that is looking for making a cool dating app bio. Craft distinctive responses for each user based on traits extracted and refer from the content provided for each trait, even if the prompts are similar.Make a single Bio for all traits at once, no need to make different for each trait. Respond it in 2nd person view. Avoid using the same sentences for different users while keeping the response within 100 words.
     {history}
     Me:{human_input}
     Jack:
@@ -65,12 +63,6 @@ trait_content_df_org=pd.read_csv('AI Personality Chart trait_content.csv')
 trait_content_df_org=trait_content_df_org.drop(0,axis=0)
 trait_content_df_org.rename(columns={'Column 1':'Question','Column 2':'Options','Column 3':'Traits','Column 4':'Content'},inplace=True)
 
-# Import the second CSV file
-traits_description_df = pd.read_csv('Traits Description - Sheet1.csv')
-
-# Extract unique values from the "Traits" column of trait_content_df_org
-unique_traits = trait_content_df_org['Traits'].unique()
-
 
 def ui():
     # Initialize a dictionary to store responses
@@ -105,11 +97,11 @@ def ui():
         st.write(question)
         st.write(selected_option)
 
-    # Extract traits from the selected options
+    # Generate a prompt based on selected options
     selected_traits = [responses[question] for question in responses]
     options_list = []
     traits_list = []
-    # content_list = []
+    content_list = []
 
     for trait_str in selected_traits:
         matching_rows = trait_content_df_org[trait_content_df_org["Options"] == trait_str]
@@ -117,24 +109,25 @@ def ui():
         if not matching_rows.empty:
             options_list.append(matching_rows["Options"].values[0])
             traits_list.append(matching_rows["Traits"].values[0])
+            content_list.append(matching_rows["Content"].values[0])
 
-    # Get unique traits from the selected options
-    unique_traits = list(set(traits_list))
-
-    # Create a content list as a formatted string
-    formatted_content_list = ",".join([f"'{trait.lower()}'" for trait in unique_traits])
+    prompt = f"The following are Traits {', '.join(traits_list)}, and the content for the options is {', '.join(content_list)}"
 
     # Display user input field
     name_input = st.text_input("Enter your name:")
 
     # Add a submit button
     if st.button("Submit"):
-        # Generate a chatbot response using the formatted content list as part of the prompt
-        prompt = f"The following are Traits {formatted_content_list}"
+        # Generate a chatbot response
         bio = conversational_chat(prompt, name_input)
         st.write(bio)
 
 
 
+
+    
+    
+
 if __name__=='__main__':
     ui()
+