Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from transformers import pipeline
+from PyPDF2 import PdfReader
+
+# Helper function to load different file types
+def load_file(uploaded_file):
+    if uploaded_file.name.endswith('.csv'):
+        return pd.read_csv(uploaded_file)
+    elif uploaded_file.name.endswith('.xlsx'):
+        return pd.read_excel(uploaded_file)
+    elif uploaded_file.name.endswith('.pdf'):
+        reader = PdfReader(uploaded_file)
+        text = ''.join(page.extract_text() for page in reader.pages)
+        # PDF parsing logic here (customized for table extraction)
+        raise NotImplementedError("PDF parsing is not implemented.")
+    else:
+        raise ValueError("Unsupported file format. Please upload CSV, Excel, or PDF.")
+
+# Train the model if it doesn't already exist
+def train_model(data):
+    data['Hour'] = pd.to_datetime(data['Timestamp']).dt.hour
+    X = data[['Hour', 'Temperature', 'CloudCover']]
+    y = data['SolarOutput']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    with open('solar_model.pkl', 'wb') as f:
+        pickle.dump(model, f)
+    return model
+
+# Load the trained model
+def load_model():
+    try:
+        with open('solar_model.pkl', 'rb') as f:
+            return pickle.load(f)
+    except FileNotFoundError:
+        return None
+
+# Initialize the text generation pipeline
+generator = pipeline('text-generation', model='gpt2', device=-1)
+
+# Streamlit app
+st.title("Smart Home Energy Advisor")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload your data file (CSV, Excel, or PDF)", type=['csv', 'xlsx', 'pdf'])
+
+if uploaded_file:
+    # Load and preprocess data
+    try:
+        data = load_file(uploaded_file)
+        st.write("Data Preview:", data.head())
+    except Exception as e:
+        st.error(f"Error loading file: {e}")
+        data = None
+
+    if data is not None:
+        # Load or train the model
+        model = load_model()
+        if not model:
+            st.warning("No pre-trained model found. Training a new model...")
+            model = train_model(data)
+            st.success("Model trained and saved successfully!")
+
+        # Preprocess data
+        data['Hour'] = pd.to_datetime(data['Timestamp']).dt.hour
+        data['PredictedSolarOutput'] = model.predict(data[['Hour', 'Temperature', 'CloudCover']])
+
+        # Determine the best hour for maximum solar energy
+        best_hour = data.loc[data['PredictedSolarOutput'].idxmax(), 'Hour']
+
+        # Generate advice
+        advice_prompt = f"The best time to use your appliances is between {best_hour}:00 and {best_hour + 2}:00."
+        advice = generator(advice_prompt, max_length=50)[0]['generated_text']
+
+        # Display predictions and advice
+        st.subheader("Predictions")
+        st.write(data)
+        st.subheader("Recommendation")
+        st.write(advice)
+
+# Chatbot feature
+st.subheader("Chat with the Advisor")
+user_query = st.text_input("Ask your question:")
+if user_query:
+    chatbot_response = generator(f"User asked: {user_query}. Response:", max_length=50)[0]['generated_text']
+    st.write("Advisor Response:", chatbot_response)