Add application file

app.py

@@ -0,0 +1,95 @@
+"""
+Requires:
+/usr/local/bin/python3.13 -m pip install python-dotenv pydantic-ai imdbpy
+OpenAI API Key
+"""
+
+from dataclasses import dataclass
+import imdb
+import asyncio
+from pydantic import BaseModel
+from pydantic_ai import Agent, RunContext
+import gradio as gr
+from pathlib import Path
+
+
+from dotenv import load_dotenv
+import os
+
+script_folder = Path(__file__).parent
+dotenv_path = script_folder/"Open_AI.env"
+load_dotenv(dotenv_path=dotenv_path)
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+
+# A pydantic basemodel for our output schema
+class Review(BaseModel):
+    why: str
+    rating: int
+    recommended: bool
+
+# A class to externally connect to the IMDB API to fetch movie information
+class IMDbConnection:
+    def __init__(self):
+        self.ia = imdb.IMDb()
+
+    async def get_movie_info(self,title: str) -> dict:
+        # Search for the movie using IMDB API
+        print(f"Searching for movie: {title}")
+        movies = self.ia.search_movie(title)
+        movie = self.ia.get_movie(movies[0].movieID)
+        system_prompt = f"""
+Title: {movie.get("title")}\n
+Rating: {movie.get("rating")}\n
+Plot: {movie.get("plot")[0] if movie.get("plot") else None}
+"""
+        print(f"System prompt: {system_prompt}\n\n")
+        return system_prompt
+
+# A container containing dependencies for the agent
+@dataclass
+class MovieData:
+    title: str
+    imdb_conn: IMDbConnection
+
+agent = Agent(
+    "openai:gpt-4o-mini",
+    deps_type=MovieData,
+    output_type=Review,
+)
+
+# Dynamically generate the system prompt
+@agent.system_prompt
+async def get_movie_info(ctx: RunContext[Review]):
+
+    # Given an input, the system prompt will be generated by querying the IMDB API
+    return await ctx.deps.imdb_conn.get_movie_info(ctx.deps.title) #Fetch movie info
+
+# create a gradio wrapper
+
+async def run_agent(user_query: str, movie_title: str):
+    deps = MovieData(title=movie_title, imdb_conn=IMDbConnection())
+    result = await agent.run(user_query, deps=deps)
+    return {
+        "Why": result.output.why,
+        "Rating": result.output.rating,
+        "Recommend": result.output.recommended
+    }
+
+#run agent in gradio
+def run_gradio(user_query, movie_title):
+    return asyncio.run(run_agent(user_query, movie_title))
+
+# build gradio ui
+with gr.Blocks() as demo:
+    gr.Markdown("## Movie Recommender Agent")
+    user_query = gr.Textbox(label="What is your preference? (e.g. 'I like supernatural horror movies')")
+    movie_title = gr.Textbox(label="Movie name (e.g. 'The Conjuring')")
+    output = gr.JSON(label = "Agent Recommendation")
+
+    submit_btn = gr.Button("Check Movie")
+    submit_btn.click(fn=run_gradio, inputs=[user_query, movie_title], outputs=output)
+
+#launch
+if __name__ == "__main__":
+    demo.launch(share=True)