Update app.py

app.py

@@ -43,28 +43,14 @@ def get_movie_info(movie_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,
-            }
+
+            return f"Title: {title}, Year: {year}, Genre: {genre}\nFind more info here: {tmdb_link}"
 
         else:
-            return {"error": "Movie not found"}
+            return "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"
+        return f"Error: {e}", ""
 
 def generate_response(prompt):
     input_text_template = (
@@ -77,23 +63,14 @@ def generate_response(prompt):
     # 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']}"
-    )
+    input_text_template += f" Movie Info: {movie_info}"
 
     model_inputs = tokenizer(input_text_template, return_tensors="pt").to(device)
 
     gen_conf = {
         "top_k": 20,
-        "max_length": 20,
+        "max_length": 20, # shortened to limit writer predictions; model loops and and prediction not coherent
         "temperature": 0.6,
         "do_sample": True,
         "eos_token_id": tokenizer.eos_token_id,
@@ -103,19 +80,14 @@ def generate_response(prompt):
 
     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
-        )
+    return f"Movie Info:\n{movie_info}\n\nGenerated Response:\n{generated_text}\n"
 
 # 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
+    response = generate_response(message)
+    history.append([message, response])
+    return response
 
 # Create Gradio Chat Interface
 chat_interface = gr.ChatInterface(chat_function)
-chat_interface.launch(share=True)
+chat_interface.launch(share=True)