Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,172 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+from pymongo import MongoClient
+import os
+from dotenv import load_dotenv
+from sklearn.ensemble import RandomForestRegressor
+import shap
+import matplotlib.pyplot as plt
+from langchain_groq import ChatGroq
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from io import BytesIO
+from streamlit_autorefresh import st_autorefresh
+
+
+
+
+
+
+# Load environment variables
+load_dotenv()
+mongo_uri = os.getenv("MONGO_URI")
+db_name = os.getenv("DB_NAME")
+collection_name = os.getenv("COLLECTION_NAME")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# MongoDB connection
+def connect_mongo():
+    client = MongoClient(mongo_uri)
+    db = client[db_name]
+    return db[collection_name]
+
+# Fetch data from MongoDB
+def get_data(collection):
+    df = pd.DataFrame(list(collection.find()))
+    if '_id' in df.columns:
+        df.drop(columns=['_id'], inplace=True)
+    return df
+
+# Train the regression model
+def train_model(X, y):
+    model = RandomForestRegressor(random_state=42)
+    model.fit(X, y)
+    return model
+
+# Generate AI Report using LangChain + Groq
+def generate_report(feature_impact, predicted_wqi, location, timestamp, selected):
+    param_info = "\n".join([f"- {param}: {selected[param]}" for param in feature_impact.keys()])
+
+    prompt = PromptTemplate.from_template(
+        """You are an expert environmental analyst.
+
+The predicted Water Quality Index (WQI) is {predicted_wqi} at location \"{location}\" on {timestamp}.
+The top contributing parameters with their actual sensor values are:
+{param_info}
+
+Write a report that includes:
+1. Likely causes for this WQI
+2. Why these parameters are significant
+3. Practical recommendations to improve WQI"""
+    )
+
+    llm = ChatGroq(groq_api_key=groq_api_key, model_name="llama-3.3-70b-versatile")
+    chain = LLMChain(llm=llm, prompt=prompt)
+
+    report = chain.run(
+        predicted_wqi=predicted_wqi,
+        location=location,
+        timestamp=timestamp,
+        param_info=param_info
+    )
+
+    report_cleaned = report.replace("**", "")
+    return report_cleaned
+
+# Function to save report as TXT
+def save_report_as_txt(text: str, filename: str) -> BytesIO:
+    buffer = BytesIO()
+    buffer.write(text.encode("utf-8"))
+    buffer.seek(0)
+    return buffer
+
+# ---------- Streamlit UI ----------
+st.set_page_config(page_title="Water Quality AI Analyzer", layout="wide")
+st.title("💧 Water Quality Index Prediction & AI-Powered Report")
+
+# Add auto-refresh using Streamlit timer
+st_autorefresh(interval=60 * 1000, key="datarefresh")
+st.markdown("⏰ Auto-refreshing every 60 seconds to fetch latest data...")
+
+# Real-time data load from MongoDB
+collection = connect_mongo()
+df = get_data(collection)
+
+if df.empty:
+    st.warning("No data found in MongoDB.")
+    st.stop()
+
+st.success("✅ Data successfully loaded from MongoDB")
+st.dataframe(df.head())
+
+# Define features and target
+feature_cols = ['pH', 'turbidity', 'dissolved_oxygen', 'conductivity', 'temperature']
+target_col = 'wqi'
+
+if not all(col in df.columns for col in feature_cols + [target_col]):
+    st.error("❌ Required columns are missing from the dataset.")
+    st.stop()
+
+
+# Train model
+X = df[feature_cols]
+y = df[target_col]
+model = train_model(X, y)
+
+# SHAP Explainer
+explainer = shap.Explainer(model, X)
+shap_values = explainer(X)
+
+# Display SHAP feature importance with smaller size
+st.subheader("📊 Feature Impact on WQI (SHAP Values)")
+fig, ax = plt.subplots(figsize=(6, 4))
+shap.summary_plot(shap_values, X, plot_type="bar", show=False)
+st.pyplot(fig)
+
+# Select record
+st.subheader("🔍 Select a Data Record for Detailed Analysis")
+record_options = [f"{i}: {row.get('location', 'Unknown')} @ {row.get('timestamp', 'N/A')}" for i, row in df.iterrows()]
+selected_label = st.selectbox("📋 Select a Record by Location & Time", options=record_options)
+selected_index = int(selected_label.split(":")[0])
+selected = df.iloc[selected_index]
+
+# Show selected record details
+st.markdown(f"🔢 Selected Index: `{selected_index}`")
+st.markdown(f"📍 Location: `{selected.get('location', 'N/A')}`")
+st.markdown(f"⏰ Timestamp: `{selected.get('timestamp', 'N/A')}`")
+
+input_data = selected[feature_cols].to_frame().T
+predicted_wqi = model.predict(input_data)[0]
+
+# Display chosen parameter values
+st.markdown("### 🧪 Selected Sensor Parameters Used for WQI Prediction")
+for param in feature_cols:
+    st.markdown(f"- **{param}**: `{selected[param]}`")
+
+# SHAP for selected row
+individual_shap = explainer(input_data)
+impact = pd.Series(individual_shap.values[0], index=feature_cols).abs().sort_values(ascending=False)
+top_impact = impact.head(3).to_dict()
+
+# Show prediction
+st.markdown(f"### 🤖 Predicted WQI: `{predicted_wqi:.2f}`")
+
+# Generate AI report and download
+if st.button("📝 Generate AI Report"):
+    location = selected.get("location", "Unknown")
+    timestamp = selected.get("timestamp", "Unknown")
+    report = generate_report(top_impact, predicted_wqi, location, timestamp, selected)
+
+    st.subheader("📝 AI-Generated Water Quality Report")
+    st.markdown(report)
+
+    # Save as TXT
+    txt_file_name = f"water_quality_report_{location.replace(' ', '_')}_{timestamp[:10]}.txt"
+    report_txt = save_report_as_txt(report, txt_file_name)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    st.download_button(
+        label="📄 Download Report (TXT)",
+        data=report_txt,
+        file_name=txt_file_name,
+        mime="text/plain"
+    )