Update app.py

app.py

@@ -14,34 +14,135 @@
 # 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
 
-# Setup API key (replace with your key or use st.secrets)
-import os
+# Set HuggingFace token from env or st.secrets
+os.environ["HUGGINGFACEHUB_API_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")
 
-HF_TOKEN = "Ayush" 
-
-# Load model
-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)