app.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import requests

model_name = "Writer/palmyra-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

def get_movie_info(movie_title):
    api_key = "20e959f0f28e6b3e3de49c50f358538a"
    search_url = f"https://api.themoviedb.org/3/search/movie"
    
    # Make a search query to TMDb
    params = {
        "api_key": api_key,
        "query": movie_title,
        "language": "en-US",
        "page": 1,
    }

    try:
        search_response = requests.get(search_url, params=params)
        search_data = search_response.json()

        # Check if any results are found
        if search_data.get("results"):
            movie_id = search_data["results"][0]["id"]

            # Fetch detailed information using the movie ID
            details_url = f"https://api.themoviedb.org/3/movie/{movie_id}"
            details_params = {
                "api_key": api_key,
                "language": "en-US",
            }

            details_response = requests.get(details_url, params=details_params)
            details_data = details_response.json()

            # Extract relevant information
            title = details_data.get("title", "Unknown Title")
            year = details_data.get("release_date", "Unknown Year")[:4]
            genre = ", ".join(genre["name"] for genre in details_data.get("genres", []))
            tmdb_link = f"https://www.themoviedb.org/movie/{movie_id}"
            # poster_path = details_data.get("poster_path")
            
            # Convert poster_path to a complete image URL
            # image_url = f"https://image.tmdb.org/t/p/w500{poster_path}" if poster_path else ""

            return {
                "title": title,
                "year": year,
                "genre": genre,
                "tmdb_link": tmdb_link,
            }

        else:
            return {"error": "Movie not found"}

    except Exception as e:
        return {"error": f"Error: {e}"}

def process_image(movie_info):
    # Process the image, return image_url
    # For now, let's just return a placeholder
    return "https://via.placeholder.com/150"

def generate_response(prompt):
    input_text_template = (
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions. "
        f"USER: {prompt} "
        "ASSISTANT:"
    )

    # Call the get_movie_info function to enrich the response
    movie_info = get_movie_info(prompt)

    if "error" in movie_info:
        return f"Error: {movie_info['error']}", None

    # Process the image separately
    image_url = process_image(movie_info)

    # Concatenate the movie info with the input template
    input_text_template += (
        f" Movie Info: Title: {movie_info['title']}, Year: {movie_info['year']}, "
        f"Genre: {movie_info['genre']}\nFind more info here: {movie_info['tmdb_link']}"
    )

    model_inputs = tokenizer(input_text_template, return_tensors="pt").to(device)

    gen_conf = {
        "top_k": 20,
        "max_length": 20,
        "temperature": 0.6,
        "do_sample": True,
        "eos_token_id": tokenizer.eos_token_id,
    }

    output = model.generate(**model_inputs, **gen_conf)

    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return (
        f"Movie Info: {movie_info['title']}, {movie_info['year']}, {movie_info['genre']}\n\n" \
        f"Generated Response: {generated_text[:100]}",  # Truncate to 100 characters
        image_url
        )

# Define chat function for gr.ChatInterface
def chat_function(message, history):
    response_text, response_image = generate_response(message)
    history.append([message, response_text])
    history.append([message, response_image])  # Separate history for image
    return response_text, response_image

# Create Gradio Chat Interface
chat_interface = gr.ChatInterface(chat_function)
chat_interface.launch(share=True)