Update app.py

app.py

@@ -1,58 +1,105 @@
-import gradio as gr
-from transformers import pipeline
-
-# pipeline 1: zero-shot to identify movie genres
-genre_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-
-# pipeline 2: text2text-generation for movie descriptions
-desc_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
-
-# fine-tuned model (pipeline 3): local movie recommender model
-rec_pipeline = pipeline("text-classification", model="model")
-
-candidate_movies = [
-    "Inception", "The Matrix", "Interstellar", "Titanic", "The Dark Knight",
-    "The Godfather", "Pulp Fiction", "The Shawshank Redemption", "Forrest Gump", "Avengers"
-]
-
-movie_genres = ["sci-fi", "drama", "romance", "action", "crime", "thriller", "adventure"]
-
-def recommend_movies(input_movies):
-    if not input_movies.strip():
-        return "⚠️ Please enter at least one movie.", []
-
-    genres = genre_classifier(input_movies, candidate_labels=movie_genres, multi_label=True)
-    top_genres = genres["labels"][:2]
-
-    # Use fine-tuned model to recommend a movie label (simplified)
-    try:
-        rec_results = rec_pipeline(f"{input_movies} | genres: {', '.join(top_genres)}")
-    except Exception as e:
-        return f"❌ Recommendation model error: {e}", []
-
-    top_rec = rec_results[:5]
-    gallery = []
-    for item in top_rec:
-        title = item["label"].replace("LABEL_", "").strip()
-        score = item["score"]
-        try:
-            desc = desc_pipeline(f"Describe the movie {title} in one sentence.", max_length=40)[0]["generated_text"]
-        except:
-            desc = "No description available."
-        img_url = f"https://via.placeholder.com/200x300?text={title.replace(' ', '+')}"
-        label = f"🎞️ **{title}**\n\n{desc}\n\nConfidence: {score:.2f}"
-        gallery.append((img_url, label))
-
-    summary = f"🎬 Based on your input and detected genres ({', '.join(top_genres)}), we recommend:"
-    return summary, gallery
-
-with gr.Blocks(title="🎥 Movie Recommender") as demo:
-    gr.Markdown("# 🎬 Personalized Movie Recommendation\n_Using Hugging Face pipelines + fine-tuned model_")
-    with gr.Row():
-        input_box = gr.Textbox(label="Enter up to 3 movies you like", placeholder="e.g. Inception, Titanic")
-        btn = gr.Button("🎯 Recommend")
-    output_text = gr.Markdown()
-    output_gallery = gr.Gallery(columns=2)
-    btn.click(fn=recommend_movies, inputs=input_box, outputs=[output_text, output_gallery])
-
-demo.launch()
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from transformers import (
+    AutoTokenizer,
+    AutoModel,
+    AutoModelForSequenceClassification
+)
+import torch
+
+# Initialize pipelines and tokenizers
+@st.cache_resource
+def load_components():
+    # Pipeline 1: Director analysis
+    director_tokenizer = AutoTokenizer.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
+    director_model = AutoModelForSequenceClassification.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
+
+    # Pipeline 2: Semantic similarity for movie recommendation
+    sim_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
+    sim_model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2")
+
+    return {
+        "director": (director_tokenizer, director_model),
+        "similarity": (sim_tokenizer, sim_model)
+    }
+
+components = load_components()
+
+# Unpack components
+director_tokenizer, director_model = components["director"]
+sim_tokenizer, sim_model = components["similarity"]
+
+# Genre mapping (translated)
+genre_mapping = {"Action": 0, "Comedy": 1, "Sci-Fi": 2, "Adventure": 3}
+
+# Sample database
+movie_db = pd.DataFrame({
+    'Title': ['Avatar', 'Interstellar', 'Jurassic Park', 'Fast & Furious 7', 'Hi, Mom'],
+    'Genre': ['Sci-Fi', 'Sci-Fi', 'Adventure', 'Action', 'Comedy'],
+    'Budget (Billion USD)': [2.37, 1.65, 0.63, 1.9, 0.15],
+    'Box Office (Billion USD)': [2.92, 0.71, 1.10, 1.51, 0.83]
+})
+
+# Pipeline: Director quality analysis
+def analyze_director(director):
+    inputs = director_tokenizer(director, return_tensors="pt")
+    with torch.no_grad():
+        outputs = director_model(**inputs)
+    scores = torch.sigmoid(outputs.logits)
+    return {
+        "Commercial Value": scores[0][0].item() * 10,
+        "Artistic Quality": scores[0][1].item() * 10
+    }
+
+# Pipeline: Movie recommendation
+def find_similar_movies(title, genre):
+    inputs = sim_tokenizer(title, padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        title_embed = sim_model(**inputs).last_hidden_state.mean(dim=1)
+
+    similarities = []
+    for _, row in movie_db.iterrows():
+        movie_inputs = sim_tokenizer(row['Title'], padding=True, truncation=True, return_tensors="pt")
+        with torch.no_grad():
+            movie_embed = sim_model(**movie_inputs).last_hidden_state.mean(dim=1)
+        sim = torch.cosine_similarity(title_embed, movie_embed)
+        similarities.append(sim.item())
+
+    movie_db['Similarity'] = similarities
+    return movie_db[movie_db['Genre'] == genre].sort_values('Similarity', ascending=False)
+
+# Streamlit Interface
+st.title("🎬 Movie Intelligence Dashboard")
+
+with st.sidebar:
+    director = st.text_input("Director Name", "Christopher Nolan")
+    title = st.text_input("Movie Title", "Inception 2") 
+    genre = st.selectbox("Genre", list(genre_mapping.keys()))
+
+if st.button("Analyze"):
+    # Director analysis
+    st.header("🧑‍💼 Director Profile")
+    director_scores = analyze_director(director)
+    fig = px.bar(
+        x=list(director_scores.keys()),
+        y=list(director_scores.values()),
+        range_y=[0, 10]
+    )
+    st.plotly_chart(fig)
+
+    # Movie recommendation
+    st.header("🎞 Recommended Movies")
+    similar_movies = find_similar_movies(title, genre)
+    st.dataframe(
+        similar_movies[['Title', 'Genre', 'Budget (Billion USD)', 'Box Office (Billion USD)', 'Similarity']],
+        column_config={
+            "Similarity": st.column_config.ProgressColumn(
+                format="%.2f",
+                min_value=0,
+                max_value=1
+            )
+        }
+    )
+