Update pages/python.py

pages/python.py

@@ -1,68 +1,164 @@
-import streamlit as st 
+import streamlit as st
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
 
+# --- Config ---
+st.set_page_config(page_title="AI Mentor Chat", layout="centered")
+st.title("🤖 AI Mentor Chat")
 
+# --- Sidebar for selections ---
+st.sidebar.title("Mentor Preferences")
+sub = st.sidebar.text_input("Enter subject:", value="Python")
+exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
+exp = st.sidebar.selectbox("Select experience:", exp1)
 
+# Map experience to label
+experience_map = {
+    '<1': 'New bie mentor',
+    '1': '1', '2': '2', '3': '3', '4': '4', '5': '5',
+    '5+': 'Professional'
+}
+experience_label = experience_map[exp]
 
+# --- Initialize Chat Model ---
+deep_seek_skeleton = HuggingFaceEndpoint(
+    repo_id='meta-llama/Llama-3.2-3B-Instruct',
+    provider='sambanova',
+    temperature=0.7,
+    max_new_tokens=150,
+    task='conversational'
+)
 
+deep_seek = ChatHuggingFace(
+    llm=deep_seek_skeleton,
+    repo_id='meta-llama/Llama-3.2-3B-Instruct',
+    provider='sambanova',
+    temperature=0.7,
+    max_new_tokens=150,
+    task='conversational'
+)
 
-import os
-import langchain
-import langchain_huggingface
-from langchain_huggingface import HuggingFaceEndpoint,HuggingFacePipeline,ChatHuggingFace
-from langchain_core.messages import HumanMessage,AIMessage,SystemMessage
+# --- Session State ---
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
 
-deep_seek_skeleton = HuggingFaceEndpoint(repo_id='meta-llama/Llama-3.2-3B-Instruct',
-                    provider = 'sambanova',
-                    temperature=0.7,
-                    max_new_tokens=150,
-                    task = 'conversational')
-deep_seek = ChatHuggingFace(llm=deep_seek_skeleton,
-                    repo_id='meta-llama/Llama-3.2-3B-Instruct',
-                    provider = 'sambanova',
-                    temperature=0.7,
-                    max_new_tokens=150,
-                    task = 'conversational')
+# --- Chat Form ---
+with st.form(key="chat_form"):
+    user_input = st.text_input("Ask your question:")
+    submit = st.form_submit_button("Send")
 
-exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
-exp = st.selectbox("Select experience:", exp1)
-if exp == '<1':
-    experince = 'New bie mentor'
-elif exp == '1':
-    experince = '1'
-elif exp == '2':
-    experince = '2'
-elif exp == '3':
-    experince = '3'
-elif exp == '4':
-    experince = '4'
-elif exp == '5':
-    experince = '5'
-elif exp == '5+':
-    experince = 'professional'
+# --- Chat Logic ---
+if submit and user_input:
+    # Add system context
+    system_prompt = f"Act as a {sub} mentor who has {experience_label} years of experience and teaches in a very friendly manner. Keep explanations within 150 words."
+
+    # Create message list
+    messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]
+
+    # Get model response
+    result = deep_seek.invoke(messages)
+
+    # Append to history
+    st.session_state.chat_history.append((user_input, result.content))
+
+# --- Display Chat History ---
+st.subheader("🗨️ Chat History")
+for i, (user, bot) in enumerate(st.session_state.chat_history):
+    st.markdown(f"**You:** {user}")
+    st.markdown(f"**Mentor:** {bot}")
+    st.markdown("---")
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+# import streamlit as st
+# import os
+# import langchain
+# import langchain_huggingface
+# from langchain_huggingface import HuggingFaceEndpoint,HuggingFacePipeline,ChatHuggingFace
+# from langchain_core.messages import HumanMessage,AIMessage,SystemMessage
+
+# deep_seek_skeleton = HuggingFaceEndpoint(repo_id='meta-llama/Llama-3.2-3B-Instruct',
+#                     provider = 'sambanova',
+#                     temperature=0.7,
+#                     max_new_tokens=150,
+#                     task = 'conversational')
+# deep_seek = ChatHuggingFace(llm=deep_seek_skeleton,
+#                     repo_id='meta-llama/Llama-3.2-3B-Instruct',
+#                     provider = 'sambanova',
+#                     temperature=0.7,
+#                     max_new_tokens=150,
+#                     task = 'conversational')
+
+# exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
+# exp = st.selectbox("Select experience:", exp1)
+# if exp == '<1':
+#     experince = 'New bie mentor'
+# elif exp == '1':
+#     experince = '1'
+# elif exp == '2':
+#     experince = '2'
+# elif exp == '3':
+#     experince = '3'
+# elif exp == '4':
+#     experince = '4'
+# elif exp == '5':
+#     experince = '5'
+# elif exp == '5+':
+#     experince = 'professional'
 
 
 
 
-selec = ['Python', 'Machine Learning', 'Deep Learning', 'Statistics', 'SQL', 'Excel']
-sub = st.selectbox("Select experience:", selec)
+# selec = ['Python', 'Machine Learning', 'Deep Learning', 'Statistics', 'SQL', 'Excel']
+# sub = st.selectbox("Select experience:", selec)
 
 
 
 
 
     
-user_input = st.text_input("Enter your query:")
-l = []
-st.write(l)
-message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
-                   HumanMessage(content=user_input)]
-while user_input!='end':
-    l.append(user_input)
-    l.append(result.content)
-    st.write(l)
-    user_input = st.text_input("Enter your query:")
-    message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
-                   HumanMessage(content=user_input)]
-    result = deep_seek.invoke(message)
+# user_input = st.text_input("Enter your query:")
+# l = []
+# st.write(l)
+# message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
+#                    HumanMessage(content=user_input)]
+# while user_input!='end':
+#     l.append(user_input)
+#     l.append(result.content)
+#     st.write(l)
+#     user_input = st.text_input("Enter your query:")
+#     message = [SystemMessage(content=f'Act as {sub} mentor who has {experince} years of experience and the one who teaches in very friendly manner and also he explains everything within 150 words'),
+#                    HumanMessage(content=user_input)]
+#     result = deep_seek.invoke(message)