Delete test.py

test.py

@@ -1,88 +0,0 @@
-import streamlit as st
-from langchain_ollama import ChatOllama
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    ChatPromptTemplate
-)
-
-st.title("🧠 BrainAI")
-st.caption("🚀 Your own AI Neurologist with SuperPowers!!")
-
-# Initiate chat engine
-llm_engine = ChatOllama(
-    model="deepseek-r1:1.5b",
-    base_url="http://localhost:11434",
-    temperature=0.3
-)
-
-# System prompt
-system_prompt = SystemMessagePromptTemplate.from_template("""
-    You are BrainAI, an AI-powered neurologist assistant designed to provide non-emergency guidance, education, 
-    and support for neurological health. Your expertise includes brain anatomy, neurological disorders (e.g., 
-    epilepsy, Alzheimer’s, brain tumors, migraines), symptoms, diagnostics, and general brain health tips. 
-    Always prioritize ethical guidelines, clarify your limitations, and emphasize consulting a licensed professional 
-    for personal care.
-""")
-
-# Session management
-if "message_log" not in st.session_state:
-    st.session_state.message_log = [{"role": "assistant", "content": "Hello! How can I assist you with brain health today?"}]
-
-# Chat container
-chat_container = st.container()
-
-# Display messages with thinking process handling
-with chat_container:
-    for message in st.session_state.message_log:
-        with st.chat_message(message["role"]):
-            # Check for thinking tags in the content
-            if "<think>" in message["content"]:
-                # Split response into thinking and actual response
-                parts = message["content"].split("</think>")
-                think_content = parts[0].replace("<think>", "").strip()
-                actual_response = parts[-1].strip()
-                
-                # Add expander for thinking process at the top
-                with st.expander("🔍 View AI's Thinking Process"):
-                    st.markdown(f"*Internal Analysis:*\n{think_content}")
-                
-                # Display actual response after expander
-                st.markdown(actual_response)
-            else:
-                st.markdown(message["content"])
-
-# Chat input
-user_query = st.chat_input(" Message ...")
-
-def generate_ai_response(prompt_chain):
-    processing_pipeline = prompt_chain | llm_engine | StrOutputParser()
-    return processing_pipeline.invoke({})
-
-def build_prompt_chain():
-    prompt_sequence = [system_prompt]
-    for msg in st.session_state.message_log:
-        if msg["role"] == "user":
-            prompt_sequence.append(HumanMessagePromptTemplate.from_template(msg["content"]))
-        elif msg["role"] == "assistant":
-            # Use original content with thinking tags if present
-            prompt_sequence.append(AIMessagePromptTemplate.from_template(msg["content"]))
-    return ChatPromptTemplate.from_messages(prompt_sequence)
-
-if user_query:
-    st.session_state.message_log.append({"role": "user", "content": user_query})
-
-    with st.spinner("🧠 Thinking ..."):
-        prompt_chain = build_prompt_chain()
-        raw_response = generate_ai_response(prompt_chain)
-        
-        # Store raw response with thinking tags
-        st.session_state.message_log.append({
-            "role": "assistant",
-            "content": raw_response
-        })
-
-    st.rerun()
-