Update app.py

app.py

@@ -1,52 +1,51 @@
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
-from transformers import TFTForConditionalGeneration, TFTTokenizer
-import torch
-
-# Function to load data and model
-@st.cache_data
-def load_data(file):
-    data = pd.read_csv(file)
-    return data
-
-# Function to predict using the model
-def predict_earthquake_positions(data, model, tokenizer):
-    inputs = tokenizer(data.to_dict(orient='list'), return_tensors="pt", padding=True, truncation=True)
-    with torch.no_grad():
-        outputs = model.generate(inputs['input_ids'], num_beams=5, early_stopping=True)
-    return outputs
-
-# Load Hugging Face model and tokenizer
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the model and tokenizer from Hugging Face
 @st.cache_resource
 def load_model():
-    model = TFTForConditionalGeneration.from_pretrained("huggingface/tft")
-    tokenizer = TFTTokenizer.from_pretrained("huggingface/tft")
-    return model, tokenizer
+    tokenizer = AutoTokenizer.from_pretrained("t5-small")  # Small T5 model for demo
+    model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+    return tokenizer, model
+
+tokenizer, model = load_model()
 
-# Streamlit App
-st.title('Earthquake Detection Prediction App')
+# Streamlit app
+st.title("Seismic Event Prediction App")
 
-uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+# File upload section
+uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])
 
 if uploaded_file is not None:
-    data = load_data(uploaded_file)
+    # Load CSV data
+    data = pd.read_csv(uploaded_file)
 
     # Display the data
-    st.subheader("Uploaded Data")
-    st.write(data)
+    st.write("## Uploaded Data")
+    st.dataframe(data)
 
-    # Load the model and tokenizer
-    model, tokenizer = load_model()
+    # Input slider for choosing an example (index between 0 and N-1)
+    st.write("## Select an example to visualize:")
+    idx = st.slider("Choose an index", 0, len(data) - 1, 0)
 
-    # Make predictions
-    st.subheader("Predictions")
-    predictions = predict_earthquake_positions(data, model, tokenizer)
+    # Prediction and plotting
+    st.write("### Selected example:", idx)
+    st.write(data.iloc[idx])
 
-    # Plotting the predictions
-    st.subheader("Earthquake Prediction Plot")
+    # Plot the predictions
     fig, ax = plt.subplots()
-    ax.plot(data['x'], data['prediction'], label="Predicted Earthquake Position", color='green')
-    ax.axvline(x=predictions, color='red', linestyle='--', label='Predicted Earthquake Start')
+    ax.plot(data['x'], label="X-axis data", color="blue")
+    ax.axvline(x=data.iloc[idx]['prediction'], color="red", label="Predicted Earthquake")
     ax.legend()
     st.pyplot(fig)
+
+    # Use the Hugging Face model to generate a simple summary or prediction based on the selected row
+    input_text = f"Predict seismic event for index {idx}."
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=50, num_beams=4, early_stopping=True)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    st.write("### Model Prediction:")
+    st.write(generated_text)