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| pip install torch torchvision torchaudio | |
| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import speech_recognition as sr | |
| # Load the Netflix dataset from CSV | |
| def load_data(): | |
| url = "https://huggingface.co/spaces/mfraz/Netflix-data/resolve/main/netflix_titles.csv" | |
| return pd.read_csv(url) | |
| # Load DialoGPT model and tokenizer | |
| def load_model(): | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") | |
| return tokenizer, model | |
| # Function to search movie details | |
| def search_movie_details(query, data): | |
| query = query.lower() | |
| search_columns = ["title", "cast", "director"] | |
| results = data.dropna(subset=search_columns) # Remove NaN values for safe searching | |
| results = results[results[search_columns].apply(lambda x: x.astype(str).str.lower().str.contains(query).any(), axis=1)] | |
| return results | |
| # Function to convert voice to text | |
| def voice_to_text(): | |
| recognizer = sr.Recognizer() | |
| with sr.Microphone() as source: | |
| st.write("🎙 Speak now...") | |
| try: | |
| audio = recognizer.listen(source, timeout=5) | |
| text = recognizer.recognize_google(audio) | |
| return text | |
| except sr.UnknownValueError: | |
| return "Sorry, I could not understand the audio." | |
| except sr.RequestError: | |
| return "Sorry, the speech service is down." | |
| # Streamlit App | |
| st.title("🎬 Netflix Movie Search Chatbot") | |
| # Load dataset and model | |
| data = load_data() | |
| tokenizer, model = load_model() | |
| # Input options: Text or Voice | |
| input_option = st.radio("Choose input method:", ("Text", "Voice")) | |
| user_input = "" | |
| if input_option == "Text": | |
| user_input = st.text_input("Enter the movie name, director, or cast:") | |
| elif input_option == "Voice": | |
| if st.button("🎤 Start Recording"): | |
| user_input = voice_to_text() | |
| st.write(f"🗣 You said: **{user_input}**") | |
| # Generate response | |
| if user_input: | |
| # Search for movie details | |
| movie_results = search_movie_details(user_input, data) | |
| if not movie_results.empty: | |
| st.write("🎥 **Here are the matching results:**") | |
| for _, row in movie_results.iterrows(): | |
| st.write(f"**📌 Title:** {row.get('title', 'N/A')}") | |
| st.write(f"**🎭 Type:** {row.get('type', 'N/A')}") | |
| st.write(f"**🎬 Director:** {row.get('director', 'N/A')}") | |
| st.write(f"**👥 Cast:** {row.get('cast', 'N/A')}") | |
| st.write(f"**📅 Release Year:** {row.get('release_year', 'N/A')}") | |
| st.write(f"**⭐ Rating:** {row.get('rating', 'N/A')}") | |
| st.write(f"**📝 Description:** {row.get('description', 'N/A')}") | |
| st.write("---") | |
| else: | |
| # Use DialoGPT for general conversation | |
| inputs = tokenizer.encode(user_input, return_tensors="pt") | |
| outputs = model.generate(inputs, max_length=100, num_return_sequences=1) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| st.write(f"🤖 **Chatbot:** {response}") | |