Upload 5 files

Dockerfile

@@ -0,0 +1,26 @@
+# Use official Python image
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy project files into the container
+COPY . /app
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port Streamlit uses
+EXPOSE 7860
+
+# Set the Streamlit app to run
+ENV STREAMLIT_PORT=7860
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# Run Streamlit
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import pandas as pd
+from recommender import load_movie_data, recommend_movies
+
+# Load data
+df = load_movie_data()
+
+st.set_page_config(page_title="Movie Chatbot", layout="centered")
+st.title("🎬 Movie Recommender Chatbot")
+st.write("Answer a few quick questions and get personalized movie suggestions!")
+
+# Step 1: Mood
+mood = st.radio("What's your mood today?", ["Feel-good", "Intense", "Thought-provoking", "Funny"])
+
+# Step 2: Genre
+genre = st.selectbox("Pick a genre:", ["Any", "Sci-Fi", "Drama", "Romance", "Action", "Mystery", "Comedy", "Animation", "Thriller"])
+
+# Step 3: Minimum rating
+min_rating = st.slider("Minimum IMDb rating:", 1.0, 10.0, 7.0)
+
+# Submit button
+if st.button("🎥 Recommend Movies"):
+    st.write("Here are some movies you might like:")
+
+    results = recommend_movies(
+        df,
+        mood=None if mood == "Any" else mood,
+        genre=None if genre == "Any" else genre,
+        min_rating=min_rating
+    )
+
+    if not results.empty:
+        for _, row in results.iterrows():
+            if pd.notna(row["poster"]):
+                st.image(row["poster"], width=200)
+
+            st.markdown(f"**🎬 {row['title']}** ({row['year']}) — *{row['genre']}*")
+            st.write(f"⭐ {row['rating']}")
+            st.markdown("---")
+    else:
+        st.warning("No movies matched your filters. Try relaxing the criteria!")

recommender.py

@@ -0,0 +1,39 @@
+import pandas as pd
+
+def load_movie_data(path="imdb_top_1000/imdb_top_1000.csv"):
+    df = pd.read_csv(path)
+    df.rename(columns={"Series_Title": "title", "Released_Year": "year", "Genre": "genre",
+        "IMDB_Rating": "rating", "Poster_Link": "poster"}, inplace=True)
+
+    # OPTIONAL: Add a fake 'mood' column based on genre (can be improved later)
+    df["mood"] = df["genre"].apply(lambda g: infer_mood_from_genre(g))
+    return df
+
+def infer_mood_from_genre(genre_str):
+    genre_str = genre_str.lower() if isinstance(genre_str, str) else ""
+    if any(g in genre_str for g in ["romance", "comedy"]):
+        return "Feel-good"
+    elif any(g in genre_str for g in ["action", "thriller"]):
+        return "Intense"
+    elif "drama" in genre_str:
+        return "Thought-provoking"
+    elif "animation" in genre_str:
+        return "Funny"
+    else:
+        return "General"
+
+def recommend_movies(df, mood=None, genre=None, min_rating=0.0):
+    filtered = df.copy()
+
+    if mood:
+        filtered = filtered[filtered["mood"].str.contains(mood, case=False, na=False)]
+
+    if genre and genre.lower() != "any":
+        filtered = filtered[filtered["genre"].str.contains(genre, case=False, na=False)]
+
+    filtered = filtered[filtered["rating"] >= min_rating]
+
+    filtered = filtered.sort_values(by="rating", ascending=False)
+
+    return filtered.head(10)
+

requirements.txt

@@ -0,0 +1,2 @@
+streamlit
+pandas