Update pages/model.py

pages/model.py

@@ -0,0 +1,111 @@
+import streamlit as st 
+import os 
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import StringIO
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+
+os.environ["HF_TOKEN"]=os.getenv('HF_Token')
+os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('HF_Token')
+
+st.set_page_config(page_title="InsightGenie – AI-Powered CSV Explorer", layout="wide")
+st.title("🧙 InsightGenie")
+st.markdown("**Explore your CSV like magic – Ask, analyze, and visualize with AI.**")
+
+if "qa_conversations" not in st.session_state:
+    st.session_state.qa_conversations = []
+
+uploaded_csv = st.file_uploader("📂 Upload your CSV file to begin", type=["csv"])
+
+if uploaded_csv:
+    try:
+        data = pd.read_csv(uploaded_csv)
+        st.success("✅ Data loaded successfully!")
+        
+        st.header("🔎 Dataset Overview")
+        st.markdown(f"- **Rows and Columns:** {data.shape[0]} rows × {data.shape[1]} columns")
+        st.markdown("**📌 Column Names:**")
+        st.write(data.columns.tolist())
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown("**🧩 Missing Values**")
+            st.dataframe(data.isnull().sum(), height=200)
+        with col2:
+            st.markdown("**🔢 Data Types**")
+            st.dataframe(data.dtypes, height=200)
+
+    except Exception as e:
+        st.error(f"❌ Failed to read the file: {e}")
+        st.stop()
+
+    st.header("💬 Ask InsightGenie")
+    user_question = st.text_input("Type your question about the dataset here:")
+
+    genie_endpoint = HuggingFaceEndpoint(
+        repo_id="deepseek-ai/DeepSeek-R1",
+        provider="nebius",
+        temperature=0.5,
+        max_new_tokens=150,
+        task="conversational"
+    )
+
+    genie_chatbot = ChatHuggingFace(
+        llm=genie_endpoint,
+        repo_id=genie_endpoint.repo_id,
+        provider=genie_endpoint.provider,
+        temperature=0.5,
+        max_new_tokens=150,
+        task="conversational"
+    )
+
+    if user_question:
+        sample_data = data.head(50).to_csv(index=False)
+
+        prompt = f"""
+You are a skilled data assistant named InsightGenie. A user has uploaded a dataset and asked a question.
+Answer clearly. If the question involves charts or graphs, provide appropriate Python code using matplotlib or seaborn.
+
+Here’s a preview of the dataset:
+{sample_data}
+
+User question:
+{user_question}
+"""
+
+        with st.spinner("🔍 Generating response..."):
+            try:
+                model_response = genie_chatbot.invoke([{"role": "user", "content": prompt}])
+                bot_reply = model_response.content if hasattr(model_response, "content") else model_response
+
+                st.session_state.qa_conversations.append((user_question, bot_reply))
+
+                st.markdown("### 🧠 Genie Says")
+                st.write(bot_reply)
+
+                # Auto-plot for simple queries
+                if "plot" in user_question.lower():
+                    with st.expander("📊 Auto-generated Plot"):
+                        try:
+                            numeric_cols = data.select_dtypes(include='number').columns.tolist()
+                            if len(numeric_cols) >= 2:
+                                fig, ax = plt.subplots()
+                                sns.lineplot(data=data, x=numeric_cols[0], y=numeric_cols[1], ax=ax)
+                                ax.set_title(f"{numeric_cols[1]} vs {numeric_cols[0]}")
+                                st.pyplot(fig)
+                            else:
+                                st.info("⚠️ Not enough numeric columns to generate a plot.")
+                        except Exception as e:
+                            st.error(f"❌ Plotting error: {e}")
+            except Exception as e:
+                st.error(f"❌ Error generating AI response: {e}")
+
+    if st.session_state.qa_conversations:
+        st.header("📚 Chat History")
+        for user_q, ai_a in reversed(st.session_state.qa_conversations):
+            st.markdown(f"**🧑‍💻 You:** {user_q}")
+            st.markdown(f"**🤖 InsightGenie:** {ai_a}")
+            st.markdown("---")
+