update app.py and requirements.txt files

app.py

@@ -1,64 +1,74 @@
-
 from dataclasses import dataclass
-import imdb
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from pydantic_ai import Agent, RunContext
 import gradio as gr
 import os
+import httpx
 
+# ---- Secrets ----
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 if not OPENAI_API_KEY:
     raise RuntimeError("Missing OPENAI_API_KEY. Add it in Spaces → Settings → Secrets.")
 
+OMDB_API_KEY = os.getenv("OMDB_API_KEY")
+if not OMDB_API_KEY:
+    raise RuntimeError("Missing OMDB_API_KEY. Add it in Spaces → Settings → Secrets.")
+
+# ---- Output schema ----
 class Review(BaseModel):
     why: str
-    rating: int
+    rating: int = Field(ge=1, le=10)
     recommended: bool
 
-class IMDbConnection:
-    def __init__(self):
-        self.ia = imdb.IMDb()
+# ---- OMDb connector ----
+class OMDbConnection:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.base_url = "https://www.omdbapi.com/"
 
     async def get_movie_info(self, title: str) -> str:
         title = (title or "").strip()
         if not title:
             return "ERROR: No title provided."
 
-        print(f"Searching for movie: {title}")
-
+        params = {"apikey": self.api_key, "t": title, "plot": "short"}
         try:
-            movies = self.ia.search_movie(title)
+            async with httpx.AsyncClient(timeout=15) as client:
+                r = await client.get(self.base_url, params=params)
+                r.raise_for_status()
+                data = r.json()
         except Exception as e:
-            return f"ERROR: IMDb search failed: {type(e).__name__}: {e}"
+            return f"ERROR: OMDb request failed: {type(e).__name__}: {e}"
 
-        if not movies:
+        if not data or data.get("Response") != "True":
+            # OMDb returns Response=False with an Error message
+            msg = data.get("Error") if isinstance(data, dict) else None
             return (
-                f"ERROR: No IMDb results found for: {title}\n"
-                "Try including the year, e.g. 'The Conjuring (2013)'."
+                f"ERROR: No OMDb results found for: {title}"
+                + (f" ({msg})" if msg else "")
+                + "\nTry adding the year, e.g. 'The Conjuring (2013)'."
             )
 
-        first = movies[0]
-        movie_id = getattr(first, "movieID", None)
-        if not movie_id:
-            return f"ERROR: IMDb result had no movieID for: {title}"
-
-        try:
-            movie = self.ia.get_movie(movie_id)
-        except Exception as e:
-            return f"ERROR: IMDb fetch failed for '{title}' (id={movie_id}): {type(e).__name__}: {e}"
-
-        return f"""Title: {movie.get("title")}
-IMDb Rating: {movie.get("rating")}
-Plot: {(movie.get("plot") or [None])[0]}
-Year: {movie.get("year")}
-Genres: {movie.get("genres")}
+        # Build system prompt from OMDb fields
+        system_prompt = f"""Title: {data.get("Title")}
+Year: {data.get("Year")}
+Rated: {data.get("Rated")}
+Runtime: {data.get("Runtime")}
+Genre: {data.get("Genre")}
+Director: {data.get("Director")}
+Actors: {data.get("Actors")}
+IMDb Rating: {data.get("imdbRating")}
+Plot: {data.get("Plot")}
 """
+        return system_prompt
 
+# ---- Deps container ----
 @dataclass
 class MovieData:
     title: str
-    imdb_conn: IMDbConnection
+    omdb_conn: OMDbConnection
 
+# ---- Agent ----
 agent = Agent(
     "openai:gpt-4o-mini",
     deps_type=MovieData,
@@ -67,13 +77,13 @@ agent = Agent(
 
 @agent.system_prompt
 async def movie_system_prompt(ctx: RunContext[MovieData]) -> str:
-    return await ctx.deps.imdb_conn.get_movie_info(ctx.deps.title)
+    return await ctx.deps.omdb_conn.get_movie_info(ctx.deps.title)
 
 async def run_agent(user_query: str, movie_title: str):
-    deps = MovieData(title=movie_title, imdb_conn=IMDbConnection())
+    deps = MovieData(title=movie_title, omdb_conn=OMDbConnection(OMDB_API_KEY))
 
-    # Preflight IMDb so we show a clean error instead of generating nonsense
-    preflight = await deps.imdb_conn.get_movie_info(movie_title)
+    # Preflight so we return clean UI errors (and avoid LLM call on missing data)
+    preflight = await deps.omdb_conn.get_movie_info(movie_title)
     if preflight.startswith("ERROR:"):
         return {"Error": preflight.replace("ERROR:", "").strip()}
 
@@ -84,14 +94,14 @@ async def run_agent(user_query: str, movie_title: str):
         "Recommend": bool(result.output.recommended),
     }
 
+# ---- Gradio UI ----
 with gr.Blocks() as demo:
-    gr.Markdown("## Movie Recommender Agent")
+    gr.Markdown("## Movie Recommender Agent (OMDb)")
     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')")
+    movie_title = gr.Textbox(label="Movie name (e.g. 'The Conjuring (2013)')")
     output = gr.JSON(label="Agent Recommendation")
-    submit_btn = gr.Button("Check Movie")
 
-    # IMPORTANT: call async fn directly (no asyncio.run)
+    submit_btn = gr.Button("Check Movie")
     submit_btn.click(fn=run_agent, inputs=[user_query, movie_title], outputs=output)
 
 if __name__ == "__main__":

requirements.txt

@@ -1,4 +1,3 @@
-imdbpy
 python-dotenv
 pydantic
 pydantic-ai