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Update app.py
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app.py
CHANGED
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@@ -330,8 +330,8 @@ if selected_app == "4) Create Chatbot":
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completion = client.chat.completions.create(
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model="gpt-3.5-turbo-0125",
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messages=[
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{"role": "system", "content": directions},
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{"role": "user", "content": prompt}
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]
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)
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@@ -387,8 +387,8 @@ if selected_app == "4) Create Chatbot":
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# Loop through each row of the DataFrame
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for index, row in df.iterrows():
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# Create a dictionary with the current question and answer
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qa_dict_quest = {"role": "user", "content": row["questions"]}
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qa_dict_ans = {"role": "assistant", "content": row["answers"]}
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# Add the dictionary to the result list
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result.append(qa_dict_quest)
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@@ -433,7 +433,7 @@ if selected_app == "4) Create Chatbot":
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)
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# Combine the random number and random string into one identifier.
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combined_string: str = f"{random_number}{random_string}"
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# Create a new collection in ChromeDB with the combined string as its name.
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collection = client.create_collection(combined_string)
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@@ -466,14 +466,14 @@ if selected_app == "4) Create Chatbot":
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collection.add(
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ids=[str(i) for i in range(0, L)], # IDs are just strings
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documents=dataset["train"]["questions"], # Enter questions here
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metadatas=[{"type": "support"} for _ in range(0, L)],
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)
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if st.session_state.curr_domain != domain:
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st.session_state.messages = []
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init_message = init_messages[domain]
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st.session_state.messages.append({"role": "assistant", "content": init_message})
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st.session_state.curr_domain = domain
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@@ -487,7 +487,7 @@ if selected_app == "4) Create Chatbot":
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# Display user message in chat message container
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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question = prompt
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@@ -496,12 +496,12 @@ if selected_app == "4) Create Chatbot":
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idx = results["ids"][0]
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idx = [int(i) for i in idx]
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ref = pd.DataFrame(
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{
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"idx": idx,
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"questions": [dataset["train"]["questions"][i] for i in idx],
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"answers": [dataset["train"]["answers"][i] for i in idx],
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"distances": results["distances"][0],
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}
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)
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# special_threshold = st.sidebar.slider('How old are you?', 0, 0.6, 0.1) # 0.3
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# special_threshold = 0.3
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@@ -518,8 +518,8 @@ if selected_app == "4) Create Chatbot":
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final_ref = ref
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engineered_prompt = f'''
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Based on the context: {ref_from_db_search},
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answer the user question: {question}.
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'''
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directions = chatbot_instructions[domain]
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@@ -533,7 +533,7 @@ if selected_app == "4) Create Chatbot":
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with st.expander("See reference:"):
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st.table(final_ref)
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": response})
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"""
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st.session_state.clear()
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completion = client.chat.completions.create(
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model="gpt-3.5-turbo-0125",
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messages=[
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{{"role": "system", "content": directions}},
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{{"role": "user", "content": prompt}}
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]
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)
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# Loop through each row of the DataFrame
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for index, row in df.iterrows():
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# Create a dictionary with the current question and answer
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qa_dict_quest = {{"role": "user", "content": row["questions"]}}
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qa_dict_ans = {{"role": "assistant", "content": row["answers"]}}
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# Add the dictionary to the result list
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result.append(qa_dict_quest)
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)
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# Combine the random number and random string into one identifier.
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combined_string: str = f"{{random_number}}{{random_string}}"
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# Create a new collection in ChromeDB with the combined string as its name.
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collection = client.create_collection(combined_string)
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collection.add(
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ids=[str(i) for i in range(0, L)], # IDs are just strings
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documents=dataset["train"]["questions"], # Enter questions here
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metadatas=[{{"type": "support"}} for _ in range(0, L)],
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)
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if st.session_state.curr_domain != domain:
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st.session_state.messages = []
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init_message = init_messages[domain]
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st.session_state.messages.append({{"role": "assistant", "content": init_message}})
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st.session_state.curr_domain = domain
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# Display user message in chat message container
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({{"role": "user", "content": prompt}})
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question = prompt
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idx = results["ids"][0]
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idx = [int(i) for i in idx]
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ref = pd.DataFrame(
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{{
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"idx": idx,
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"questions": [dataset["train"]["questions"][i] for i in idx],
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"answers": [dataset["train"]["answers"][i] for i in idx],
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"distances": results["distances"][0],
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}}
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)
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# special_threshold = st.sidebar.slider('How old are you?', 0, 0.6, 0.1) # 0.3
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# special_threshold = 0.3
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final_ref = ref
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engineered_prompt = f'''
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Based on the context: {{ref_from_db_search}},
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answer the user question: {{question}}.
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'''
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directions = chatbot_instructions[domain]
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with st.expander("See reference:"):
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st.table(final_ref)
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# Add assistant response to chat history
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st.session_state.messages.append({{"role": "assistant", "content": response}})
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"""
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st.session_state.clear()
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