Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,98 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import numpy as np
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="Disaster Tweet Classifier",
+    page_icon="🚨",
+    layout="centered"
+)
+
+# --- Title and Description ---
+st.title("🚨 Disaster Tweet Classifier")
+st.markdown("""
+This AI application determines whether a tweet is about a **real disaster** (e.g., earthquake, fire) 
+or just uses **metaphorical language** (e.g., 'This movie was a disaster').
+\n**Powered by:** BERT (Bidirectional Encoder Representations from Transformers)
+""")
+
+# --- Model Loading Logic ---
+# @st.cache_resource is critical! It ensures the model is loaded only once,
+# not every time a user interacts with the app.
+@st.cache_resource
+def load_model():
+    # Since files (tf_model.h5, config.json, vocab.txt) are in the root directory
+    model_path = "./" 
+    
+    try:
+        # Load Tokenizer and Model
+        tokenizer = BertTokenizer.from_pretrained(model_path)
+        model = TFBertForSequenceClassification.from_pretrained(model_path)
+        return tokenizer, model
+    except Exception as e:
+        st.error(f"❌ Error loading model: {e}")
+        st.info("Make sure you uploaded all files (config.json, tf_model.h5, vocab.txt) to the root directory.")
+        return None, None
+
+# Load the model immediately
+with st.spinner("Loading AI Model... Please wait..."):
+    tokenizer, model = load_model()
+
+# --- Prediction Logic ---
+def predict_tweet(text, tokenizer, model, max_len=80):
+    # 1. Prepare text for BERT
+    encoded = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        max_length=max_len,
+        padding='max_length',
+        return_attention_mask=True,
+        truncation=True
+    )
+    
+    # Convert to Tensor (Batch size of 1)
+    input_ids = tf.convert_to_tensor([encoded['input_ids']])
+    attention_masks = tf.convert_to_tensor([encoded['attention_mask']])
+    
+    # 2. Predict (Get Logits)
+    prediction = model([input_ids, attention_masks]) # Using direct call instead of .predict for speed
+    logits = prediction.logits
+    
+    # 3. Softmax to get probabilities
+    probs = tf.nn.softmax(logits, axis=1).numpy()[0]
+    
+    # 4. Result
+    pred_class = np.argmax(probs) # 0 or 1
+    confidence = probs[pred_class]
+    
+    return pred_class, confidence
+
+# --- User Interface ---
+st.divider()
+user_input = st.text_area("Enter a tweet below:", height=100, placeholder="Example: The forest fire is spreading rapidly!")
+
+col1, col2 = st.columns([1, 2])
+with col1:
+    analyze_button = st.button("Analyze Tweet", type="primary", use_container_width=True)
 
-"""
-# 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])),
-    ))
+if analyze_button:
+    if not user_input.strip():
+        st.warning("⚠️ Please enter some text first.")
+    elif model is None:
+        st.error("Model is not loaded properly.")
+    else:
+        # Run Prediction
+        pred_class, confidence = predict_tweet(user_input, tokenizer, model)
+        
+        # Display Output
+        st.divider()
+        if pred_class == 1:
+            st.error(f"### 🚨 Prediction: REAL DISASTER")
+            st.write(f"Confidence Score: **%{confidence * 100:.2f}**")
+            st.progress(int(confidence * 100))
+        else:
+            st.success(f"### 😌 Prediction: NOT A DISASTER")
+            st.write(f"Confidence Score: **%{confidence * 100:.2f}**")
+            st.progress(int(confidence * 100))