Upload 4 files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    python3-dev \
+    libatlas-base-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy files
+COPY requirements.txt .
+COPY main.py .
+COPY prophet_model.pkl .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose Streamlit port
+EXPOSE 7860
+
+# Run Streamlit app
+CMD ["streamlit", "run", "main.py", "--server.port=7860", "--server.address=0.0.0.0"]

main.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import matplotlib.pyplot as plt
+
+# --------------------
+# LOAD TRAINED MODEL
+# --------------------
+MODEL_PATH = "prophet_model.pkl"  # updated to match your file
+
+try:
+    with open(MODEL_PATH, "rb") as f:
+        model = pickle.load(f)
+    st.success("✅ Loaded saved Prophet model.")
+except FileNotFoundError:
+    model = None
+    st.warning("⚠ No saved model found. You can still upload CSV for chart display.")
+
+# --------------------
+# UPLOAD CSV FILE
+# --------------------
+st.title("📊 Prophet Forecast Viewer")
+uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file)
+
+    st.subheader("Data Preview")
+    st.write(df.head())
+
+    # Prepare data for Prophet if model exists
+    if model is not None:
+        try:
+            # Ensure Prophet format
+            df = df.rename(columns={df.columns[0]: 'ds'})
+            df['ds'] = pd.to_datetime(df['ds'])
+
+            predictions = model.predict(df)
+            st.subheader("Predictions")
+            st.write(predictions[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head())
+
+            # Plot
+            fig, ax = plt.subplots(figsize=(10, 5))
+            ax.plot(df['ds'], predictions['yhat'], label="Forecast", color="blue")
+            ax.fill_between(df['ds'], predictions['yhat_lower'], predictions['yhat_upper'], alpha=0.2)
+            ax.set_title("Forecast Chart")
+            ax.set_xlabel("Date")
+            ax.set_ylabel("Value")
+            ax.legend()
+            st.pyplot(fig)
+
+        except Exception as e:
+            st.error(f"Prediction failed: {e}")
+
+else:
+    st.info("📂 Please upload a CSV file to begin.")

prophet_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a788c260920ff23dd0bdd6210821347459e758e4d99b51dc59b1f663951e01a2
+size 100954

requirements.txt

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+streamlit
+pandas
+matplotlib
+prophet