Update app/streamlit_app.py

app/streamlit_app.py

@@ -1,136 +1,136 @@
-# app/streamlit_app.py
-
-import streamlit as st
-import requests
-import json
-import pandas as pd
-import altair as alt
-import time
-import subprocess
-import sys
-from pathlib import Path
-
-# Add root to sys.path for imports if needed
-sys.path.append(str(Path(__file__).resolve().parent.parent))
-
-# ---- Constants ----
-# API_URL = "http://127.0.0.1:8000/predict"
-API_URL = "http://localhost:8000/predict""
-CUSTOM_DATA_PATH = Path(__file__).parent.parent / "data" / "custom_upload.csv"
-METADATA_PATH = Path(__file__).parent.parent / "model" / "metadata.json"
-ACTIVITY_LOG_PATH = Path(__file__).parent.parent / "logs" / "activity_log.json"
-DRIFT_LOG_PATH = Path(__file__).parent.parent / "logs" / "monitoring_log.json"
-
-# ---- Streamlit UI ----
-st.set_page_config(page_title="Fake News Detector", layout="centered")
-st.title("📰 Fake News Detector")
-st.markdown("Enter a news article's headline or content to predict if it's **Fake** or **Real**.")
-
-# ---- Prediction Form ----
-with st.form(key="predict_form"):
-    user_input = st.text_area("News Text", height=150)
-    submit = st.form_submit_button("🧠 Predict")
-
-if submit:
-    if not user_input.strip():
-        st.warning("Please enter some text.")
-    else:
-        try:
-            response = requests.post(API_URL, json={"text": user_input})
-            if response.status_code == 200:
-                result = response.json()
-                pred = result["prediction"]
-                prob = result["confidence"]
-                st.success(f"🧾 Prediction: **{pred}**")
-                st.info(f"📈 Confidence: {prob * 100:.2f}%")
-            else:
-                st.error(f"API Error: {response.status_code}")
-        except Exception as e:
-            st.error(f"❌ Failed to connect to FastAPI: {e}")
-
-# ---- Upload + Train ----
-st.header("📤 Train with Your Own CSV")
-
-with st.expander("Upload CSV to Retrain Model (columns: `text`, `label`)"):
-    uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
-    if uploaded_file:
-        try:
-            df_custom = pd.read_csv(uploaded_file)
-            if "text" not in df_custom.columns or "label" not in df_custom.columns:
-                st.error("CSV must contain 'text' and 'label' columns.")
-            else:
-                st.success("✅ File looks good. Starting training...")
-
-                # Save CSV
-                df_custom.to_csv(CUSTOM_DATA_PATH, index=False)
-
-                # Progress bar animation
-                progress_bar = st.progress(0)
-                status_text = st.empty()
-                for percent in range(0, 101, 10):
-                    progress_bar.progress(percent)
-                    status_text.text(f"Training Progress: {percent}%")
-                    time.sleep(0.2)
-
-                # Trigger training subprocess
-                result = subprocess.run(
-                    [sys.executable, "model/train.py", "--data_path", str(CUSTOM_DATA_PATH), "--output_path", "model/custom_model.pt"],
-                    capture_output=True, text=True
-                )
-
-                if result.returncode == 0:
-                    acc = float(result.stdout.strip())
-                    new_version = "custom_" + time.strftime("%H%M%S")
-                    metadata = {
-                        "model_version": new_version,
-                        "test_accuracy": round(acc, 4),
-                        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S")
-                    }
-                    with open(METADATA_PATH, "w") as f:
-                        json.dump(metadata, f, indent=2)
-                    status_text.text("🎉 Training complete!")
-                    st.success(f"New model trained with accuracy: {acc:.4f}")
-                else:
-                    st.error("Training failed.")
-                    st.text(result.stderr)
-        except Exception as e:
-            st.error(f"Error reading file: {e}")
-
-# ---- Sidebar Info ----
-st.sidebar.header("📊 Model Info")
-if METADATA_PATH.exists():
-    with open(METADATA_PATH) as f:
-        meta = json.load(f)
-    st.sidebar.markdown(f"**Version**: `{meta['model_version']}`")
-    st.sidebar.markdown(f"**Accuracy**: `{meta['test_accuracy']}`")
-    st.sidebar.markdown(f"**Updated**: `{meta['timestamp'].split('T')[0]}`")
-else:
-    st.sidebar.warning("No metadata found.")
-
-# ---- Activity Log ----
-st.sidebar.header("📜 Activity Log")
-if ACTIVITY_LOG_PATH.exists():
-    with open(ACTIVITY_LOG_PATH) as f:
-        activity_log = json.load(f)
-    for entry in reversed(activity_log[-5:]):
-        st.sidebar.text(f"{entry['timestamp']} - {entry['event']}")
-else:
-    st.sidebar.info("No recent logs found.")
-
-# ---- Drift Chart ----
-st.sidebar.header("📉 Drift Monitoring")
-if DRIFT_LOG_PATH.exists():
-    drift_df = pd.read_json(DRIFT_LOG_PATH)
-    drift_df["timestamp"] = pd.to_datetime(drift_df["timestamp"])
-    drift_df["status"] = drift_df["drift_detected"].map({True: "Drift", False: "Stable"})
-
-    chart = alt.Chart(drift_df).mark_line(point=True).encode(
-        x="timestamp:T",
-        y=alt.Y("test_accuracy:Q", title="Test Accuracy"),
-        color="status:N",
-        tooltip=["timestamp", "test_accuracy", "status"]
-    ).properties(title="Model Performance & Drift", height=250)
-
-    st.sidebar.altair_chart(chart, use_container_width=True)
-else:
-    st.sidebar.info("No drift data available.")
+# app/streamlit_app.py
+
+import streamlit as st
+import requests
+import json
+import pandas as pd
+import altair as alt
+import time
+import subprocess
+import sys
+from pathlib import Path
+
+# Add root to sys.path for imports if needed
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+
+# ---- Constants ----
+# API_URL = "http://127.0.0.1:8000/predict"
+API_URL = "http://localhost:8000/predict"
+CUSTOM_DATA_PATH = Path(__file__).parent.parent / "data" / "custom_upload.csv"
+METADATA_PATH = Path(__file__).parent.parent / "model" / "metadata.json"
+ACTIVITY_LOG_PATH = Path(__file__).parent.parent / "logs" / "activity_log.json"
+DRIFT_LOG_PATH = Path(__file__).parent.parent / "logs" / "monitoring_log.json"
+
+# ---- Streamlit UI ----
+st.set_page_config(page_title="Fake News Detector", layout="centered")
+st.title("📰 Fake News Detector")
+st.markdown("Enter a news article's headline or content to predict if it's **Fake** or **Real**.")
+
+# ---- Prediction Form ----
+with st.form(key="predict_form"):
+    user_input = st.text_area("News Text", height=150)
+    submit = st.form_submit_button("🧠 Predict")
+
+if submit:
+    if not user_input.strip():
+        st.warning("Please enter some text.")
+    else:
+        try:
+            response = requests.post(API_URL, json={"text": user_input})
+            if response.status_code == 200:
+                result = response.json()
+                pred = result["prediction"]
+                prob = result["confidence"]
+                st.success(f"🧾 Prediction: **{pred}**")
+                st.info(f"📈 Confidence: {prob * 100:.2f}%")
+            else:
+                st.error(f"API Error: {response.status_code}")
+        except Exception as e:
+            st.error(f"❌ Failed to connect to FastAPI: {e}")
+
+# ---- Upload + Train ----
+st.header("📤 Train with Your Own CSV")
+
+with st.expander("Upload CSV to Retrain Model (columns: `text`, `label`)"):
+    uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
+    if uploaded_file:
+        try:
+            df_custom = pd.read_csv(uploaded_file)
+            if "text" not in df_custom.columns or "label" not in df_custom.columns:
+                st.error("CSV must contain 'text' and 'label' columns.")
+            else:
+                st.success("✅ File looks good. Starting training...")
+
+                # Save CSV
+                df_custom.to_csv(CUSTOM_DATA_PATH, index=False)
+
+                # Progress bar animation
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+                for percent in range(0, 101, 10):
+                    progress_bar.progress(percent)
+                    status_text.text(f"Training Progress: {percent}%")
+                    time.sleep(0.2)
+
+                # Trigger training subprocess
+                result = subprocess.run(
+                    [sys.executable, "model/train.py", "--data_path", str(CUSTOM_DATA_PATH), "--output_path", "model/custom_model.pt"],
+                    capture_output=True, text=True
+                )
+
+                if result.returncode == 0:
+                    acc = float(result.stdout.strip())
+                    new_version = "custom_" + time.strftime("%H%M%S")
+                    metadata = {
+                        "model_version": new_version,
+                        "test_accuracy": round(acc, 4),
+                        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S")
+                    }
+                    with open(METADATA_PATH, "w") as f:
+                        json.dump(metadata, f, indent=2)
+                    status_text.text("🎉 Training complete!")
+                    st.success(f"New model trained with accuracy: {acc:.4f}")
+                else:
+                    st.error("Training failed.")
+                    st.text(result.stderr)
+        except Exception as e:
+            st.error(f"Error reading file: {e}")
+
+# ---- Sidebar Info ----
+st.sidebar.header("📊 Model Info")
+if METADATA_PATH.exists():
+    with open(METADATA_PATH) as f:
+        meta = json.load(f)
+    st.sidebar.markdown(f"**Version**: `{meta['model_version']}`")
+    st.sidebar.markdown(f"**Accuracy**: `{meta['test_accuracy']}`")
+    st.sidebar.markdown(f"**Updated**: `{meta['timestamp'].split('T')[0]}`")
+else:
+    st.sidebar.warning("No metadata found.")
+
+# ---- Activity Log ----
+st.sidebar.header("📜 Activity Log")
+if ACTIVITY_LOG_PATH.exists():
+    with open(ACTIVITY_LOG_PATH) as f:
+        activity_log = json.load(f)
+    for entry in reversed(activity_log[-5:]):
+        st.sidebar.text(f"{entry['timestamp']} - {entry['event']}")
+else:
+    st.sidebar.info("No recent logs found.")
+
+# ---- Drift Chart ----
+st.sidebar.header("📉 Drift Monitoring")
+if DRIFT_LOG_PATH.exists():
+    drift_df = pd.read_json(DRIFT_LOG_PATH)
+    drift_df["timestamp"] = pd.to_datetime(drift_df["timestamp"])
+    drift_df["status"] = drift_df["drift_detected"].map({True: "Drift", False: "Stable"})
+
+    chart = alt.Chart(drift_df).mark_line(point=True).encode(
+        x="timestamp:T",
+        y=alt.Y("test_accuracy:Q", title="Test Accuracy"),
+        color="status:N",
+        tooltip=["timestamp", "test_accuracy", "status"]
+    ).properties(title="Model Performance & Drift", height=250)
+
+    st.sidebar.altair_chart(chart, use_container_width=True)
+else:
+    st.sidebar.info("No drift data available.")