Update app.py

app.py

@@ -1,165 +1,18 @@
-# import os
-# import langchain
-# import langchain_huggingface
-# from langchain_huggingface import HuggingFaceEndpoint,HuggingFacePipeline, ChatHuggingFace
-# from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
-
-# os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('Ayush')
-
-# llama_model = HuggingFaceEndpoint(repo_id= "meta-llama/Llama-3.2-3B-Instruct",provider= "nebius",temperature=0.6, max_new_tokens=70,task="conversational")
-# model_d=ChatHuggingFace(llm =llama_model,repo_id= "meta-llama/Llama-3.2-3B-Instruct",provider= "nebius",temperature=0.6, max_new_tokens=70,task="conversational")
-# message = [SystemMessage(content = "Answer like you are a hardcore pc gamer"),
-#            HumanMessage(content = "Give me name of top 10 pc games of all time with description")]
-# result = model_d.invoke(message)
-# print(result.content)
-
-# import os
-# import streamlit as st
-# from langchain_community.chat_models import ChatHuggingFace
-# from langchain_community.llms import HuggingFaceHub
-# from langchain_core.messages import HumanMessage, SystemMessage
-# from fpdf import FPDF
-
-# # Set HuggingFace token from env or st.secrets
-# os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("keys")
-# os.environ["HF_TOKEN"]=os.getenv('Ayush')
-
-
-# # Topic-wise base prompts and models
-# topic_config = {
-#     "Python": {
-#         "prompt": "Answer like a senior Python developer and coding mentor.",
-#         "model": "meta-llama/Llama-3.2-3B-Instruct"
-#     },
-#     "SQL": {
-#         "prompt": "Answer like a senior SQL engineer with industry experience.",
-#         "model": "google/gemma-3-27b-it"
-#     },
-#     "Power BI": {
-#         "prompt": "Answer like a Power BI expert helping a beginner.",
-#         "model": "mistralai/Mistral-7B-Instruct-v0.1"
-#     },
-#     "Statistics": {
-#         "prompt": "Answer like a statistics professor explaining key concepts to a student.",
-#         "model": "deepseek-ai/DeepSeek-R1"
-#     },
-#     "Machine Learning": {
-#         "prompt": "Answer like an ML mentor guiding a junior data scientist.",
-#         "model": "google/gemma-3-27b-it"
-#     },
-#     "Deep Learning": {
-#         "prompt": "Answer like a deep learning researcher with real-world insights.",
-#         "model": "meta-llama/Llama-3.2-3B-Instruct"
-#     },
-#     "Generative AI": {
-#         "prompt": "Answer like an expert in LLMs and Generative AI research.",
-#         "model": "deepseek-ai/DeepSeek-R1"
-#     }
-# }
-
-# # Experience level adjustments to prompt
-# experience_prompts = {
-#     "Beginner": "Explain with simple language and clear examples for a beginner.",
-#     "Intermediate": "Provide a detailed answer suitable for an intermediate learner.",
-#     "Expert": "Give an in-depth and advanced explanation suitable for an expert."
-# }
-
-# # Streamlit app setup
-# st.set_page_config(page_title="Data Science Mentor", page_icon="📘")
-# st.title("📘 Data Science Mentor App")
-
-# if "chat_history" not in st.session_state:
-#     st.session_state.chat_history = []
-
-# # Multi-select topics
-# selected_topics = st.multiselect("Select one or more topics:", list(topic_config.keys()), default=["Python"])
-
-# # Select experience level
-# experience_level = st.selectbox("Select mentor experience level:", list(experience_prompts.keys()))
-
-# question = st.text_area("Ask your question here:")
-
-# if st.button("Get Answer"):
-#     if not selected_topics:
-#         st.warning("Please select at least one topic.")
-#     elif not question.strip():
-#         st.warning("Please enter your question.")
-#     else:
-#         # Combine prompts from selected topics + experience level
-#         combined_prompt = ""
-#         models_used = set()
-#         for topic in selected_topics:
-#             base_prompt = topic_config[topic]["prompt"]
-#             combined_prompt += f"{base_prompt} "
-#             models_used.add(topic_config[topic]["model"])
-
-#         combined_prompt += experience_prompts[experience_level]
-
-#         # Choose the first model from selected topics (or could do more advanced merging)
-#         chosen_model = list(models_used)[0]
-
-#         # Load model
-#         llm = HuggingFaceHub(
-#             repo_id=chosen_model,
-#             model_kwargs={"temperature": 0.6, "max_new_tokens": 150}
-#         )
-#         chat_model = ChatHuggingFace(llm=llm)
-
-#         messages = [
-#             SystemMessage(content=combined_prompt),
-#             HumanMessage(content=question)
-#         ]
-
-#         with st.spinner("Mentor is typing..."):
-#             response = chat_model.invoke(messages)
-
-#         st.markdown("### 🧠 Mentor's Response:")
-#         st.markdown(response.content)
-
-#         # Save chat
-#         st.session_state.chat_history.append((selected_topics, experience_level, question, response.content))
-
-# # Display chat history
-# if st.session_state.chat_history:
-#     st.markdown("---")
-#     st.subheader("📝 Chat History")
-#     for i, (topics, exp, q, a) in enumerate(st.session_state.chat_history, 1):
-#         st.markdown(f"**{i}. Topics:** {', '.join(topics)} | **Mentor Level:** {exp}")
-#         st.markdown(f"**You:** {q}")
-#         st.markdown(f"**Mentor:** {a}")
-#         st.markdown("---")
-
-#     # Download PDF
-#     if st.button("📄 Download PDF of this chat"):
-#         pdf = FPDF()
-#         pdf.add_page()
-#         pdf.set_font("Arial", size=12)
-
-#         pdf.cell(200, 10, txt="Data Science Mentor Chat History", ln=True, align="C")
-#         pdf.ln(10)
-
-#         for i, (topics, exp, q, a) in enumerate(st.session_state.chat_history, 1):
-#             pdf.multi_cell(0, 10, f"{i}. Topics: {', '.join(topics)} | Mentor Level: {exp}\nYou: {q}\nMentor: {a}\n\n")
-
-#         pdf_path = "/tmp/mentor_chat.pdf"
-#         pdf.output(pdf_path)
-
-#         with open(pdf_path, "rb") as f:
-#             st.download_button("📥 Click to Download PDF", f, file_name="mentor_chat.pdf", mime="application/pdf")
-
-
 import streamlit as st
+import os
 from langchain_community.chat_models import ChatHuggingFace
 from langchain_community.llms import HuggingFaceHub
 from langchain_core.messages import HumanMessage, SystemMessage
 
 # ✅ Load your secret token from Hugging Face Space secrets
-HF_TOKEN = st.secrets["Final_key"]
+hf_token = os.getenv("Data_science")  # Make sure "Data_science" is set in Space secrets
+os.environ["HUGGINGFACEHUB_API_KEY"] = hf_token
+os.environ["HF_TOKEN"] = hf_token
 
 # ✅ Initialize the LLM with your token
 llm = HuggingFaceHub(
     repo_id="meta-llama/Llama-3.2-3B-Instruct",
-    huggingfacehub_api_token=HF_TOKEN,
+    huggingfacehub_api_token=hf_token,  # <-- use hf_token here
     model_kwargs={"temperature": 0.6, "max_new_tokens": 100}
 )
 
@@ -170,7 +23,7 @@ st.title("🧠 LLaMA Chat (Data Science Mentor)")
 
 question = st.text_input("Ask any question about data science topics:")
 
-if st.button("Ask"):
+if st.button("Ask") and question.strip():
     messages = [
         SystemMessage(content="You are a data science mentor."),
         HumanMessage(content=question)
@@ -178,35 +31,3 @@ if st.button("Ask"):
     response = chat_model.invoke(messages)
     st.write("### Mentor's Response:")
     st.write(response.content)
-
-
-# import streamlit as st
-# from langchain_community.chat_models import ChatHuggingFace
-# from langchain_community.llms import HuggingFaceHub
-# from langchain_core.messages import HumanMessage, SystemMessage
-
-# # Directly enter or securely load your Hugging Face API token
-# HF_TOKEN = "your_huggingface_token_here"  # 🔐 Replace with your token or use st.secrets
-
-# # Load model with token explicitly passed
-# llm = HuggingFaceHub(
-#     repo_id="meta-llama/Llama-3.2-3B-Instruct",
-#     huggingfacehub_api_token=HF_TOKEN,
-#     model_kwargs={"temperature": 0.6, "max_new_tokens": 100}
-# )
-
-# chat_model = ChatHuggingFace(llm=llm)
-
-# # Streamlit UI
-# st.title("🧪 Simple LLaMA Chat Test")
-
-# question = st.text_input("Ask a gaming-related question:", "Give me name of top 10 PC games of all time with description")
-
-# if st.button("Ask"):
-#     messages = [
-#         SystemMessage(content="Answer like you are a hardcore PC gamer"),
-#         HumanMessage(content=question)
-#     ]
-#     response = chat_model.invoke(messages)
-#     st.write("### Response:")
-#     st.write(response.content)