Update app.py

app.py

@@ -13,137 +13,199 @@
 # result = model_d.invoke(message)
 # print(result.content)
 
-import os
+# 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
 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")
 
+# 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)
+
+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)