Create app.py

app.py

@@ -0,0 +1,111 @@
+import os
+import streamlit as st
+from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+# Set Hugging Face API token securely
+HF_TOKEN = os.getenv("key")
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN
+os.environ["HF_TOKEN"] = HF_TOKEN
+
+# ---------- Page Configuration ----------
+st.set_page_config(page_title="Innomatics Online Trainer Bot", page_icon="🧠", layout="centered")
+
+# ---------- Custom CSS ----------
+st.markdown("""
+<style>
+    .main {background-color: transparent; padding: 20px;}
+    .stButton>button {
+        background-color: white; color: #8B0000; border: 2px solid white;
+        border-radius: 10px; padding: 10px 20px; font-size: 18px;
+        font-weight: bold; width: 100%; transition: 0.3s ease-in-out;
+    }
+    .stButton>button:hover {
+        background-color: #8B0000; color: white; border: 2px solid white;
+    }
+    h1, h2, h3, p, div, span, label, input, textarea {
+        color: black !important;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# ---------- UI Header ----------
+st.markdown("<h1 style='text-align: center'>Innomatics AI Mentor Bot 🧠</h1>", unsafe_allow_html=True)
+st.markdown("### 👋 Welcome to the Innomatics AI Mentor Bot 🤖")
+st.markdown("""
+This dashboard provides an AI mentor that gives instant, skill-adapted help 
+with Python, SQL, PowerBI, and data science to guide you through module doubts.
+""")
+st.markdown("## In which module do you have doubt?")
+
+# ---------- Module Buttons ----------
+modules = {
+    "Python": "🐍",
+    "SQL": "🗃️",
+    "PowerBI": "📊",
+    "Statistics": "📈",
+    "Machine_Learning": "🤖",
+    "Deep_Learning": "🧠"
+}
+
+cols = st.columns(3)
+for i, (module, emoji) in enumerate(modules.items()):
+    if cols[i % 3].button(f"{emoji} {module}", key=f"{module}_btn"):
+        st.session_state.mentor_type = module
+        st.session_state.mentor_emoji = emoji
+
+# ---------- Session State Defaults ----------
+st.session_state.setdefault("mentor_type", None)
+st.session_state.setdefault("mentor_emoji", "🧠")
+
+# ---------- Chat Interface ----------
+if st.session_state.mentor_type:
+    mentor = st.session_state.mentor_type
+    emoji = st.session_state.mentor_emoji
+
+    st.subheader(f"{emoji} {mentor.upper()} Mentor Chat")
+    experience = st.slider("Your experience (in years):", 0, 20, 1)
+    user_input = st.text_input("Ask your question:")
+    output_container = st.empty()
+
+    # Select HuggingFace model based on module
+    model_map = {
+        "Python": ("meta-llama/Llama-3.1-8B-Instruct", "nebius"),
+        "SQL": ("deepseek-ai/DeepSeek-R1", "nebius"),
+        "PowerBI": ("deepseek-ai/DeepSeek-R1", "nebius"),
+        "Statistics": ("meta-llama/Llama-3.2-1B-Instruct", "nebius"),
+        "Machine_Learning": ("meta-llama/Llama-3.3-70B-Instruct", "nebius"),
+        "Deep_Learning": ("meta-llama/Meta-Llama-3-70B-Instruct", "hyperbolic")
+    }
+
+    repo_id, provider = model_map.get(mentor, (None, None))
+    if repo_id:
+        model = HuggingFaceEndpoint(repo_id=repo_id, provider=provider, temperature=0.5, max_new_tokens=150)
+        chat_model = ChatHuggingFace(llm=model)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            if st.button("🚀 Ask", key="ask_btn"):
+                if user_input:
+                    prompt = ChatPromptTemplate.from_messages([
+                        SystemMessagePromptTemplate.from_template(
+                            f"You are a helpful and experienced {mentor.upper()} mentor {emoji} assisting a learner with {experience} years of experience."
+                        ),
+                        HumanMessagePromptTemplate.from_template("{question}")
+                    ])
+                    formatted_prompt = prompt.format_messages(question=user_input)
+
+                    with st.spinner(f"{emoji} Mentor is thinking..."):
+                        try:
+                            response = chat_model.invoke(formatted_prompt)
+                            output_container.markdown(f"**👤 You:** {user_input}")
+                            output_container.markdown(f"**{emoji} Mentor:** {response.content}")
+                        except Exception as e:
+                            output_container.error(f"❌ An error occurred: {str(e)}")
+                else:
+                    output_container.warning("⚠️ Please enter a question first!")
+
+        with col2:
+            if st.button("🧹 Clear", key="clear_btn"):
+                output_container.empty()