Update app.py

app.py

@@ -1,90 +1,91 @@
-# !pip install --upgrade llama-index llama-index-embeddings-huggingface llama-index-llms-huggingface transformers torch accelerate bitsandbytes
-
-from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex, Document
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.llms.huggingface import HuggingFaceLLM
-import torch
-import os
-import pandas as pd
-import gradio as gr
-
-Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-
-Settings.embed_model = HuggingFaceEmbedding(
-    model_name="sentence-transformers/all-MiniLM-L6-v2")
-Settings.llm = HuggingFaceLLM(
-    model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-    tokenizer_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-    context_window=2048,
-    max_new_tokens=256,
-    device_map="auto",
-    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}
-)
-
-csv_file_path = "movie_recommendations_with_names.csv"
-df = None
-
-try:
-    df = pd.read_csv(csv_file_path)
-except FileNotFoundError:
-    print(f"Error: CSV file not found at {csv_file_path}")
-    print(f"Current working directory: {os.getcwd()}")
-
-except Exception as e:
-    print(f"An unexpected error occurred while reading the CSV: {e}")
-    
-if df is not None:
-    movies_data = [
-        Document(text=f"MovieID: {row['movie_id']}, Title: {row['title']}, Genre: {row['genre']}, Rating: {row['rating']}",
-                 metadata={"movie_id": row['movie_id'], "title": row['title'], "genre": row['genre'], "rating": row['rating']})
-        for index, row in df.iterrows()
-    ]
-index = VectorStoreIndex.from_documents(movies_data)
-query_engine = index.as_query_engine()
-
-def recommend_movie(genre):
-    if not genre.strip():
-        return "! Please enter a movie genre."
-    
-    response = query_engine.query(
-        f"List titles and genre of movies with genre {genre}."
-        f"provide at least 3 recommendations if avalabile."
-    )
-    
-    response_lines= str(response).split("\n")
-    filtered = [
-        line for line in response_lines
-        if line.strip()
-        and "Note : The query is not specific" not in line
-        ]
-    
-    recommendations = []
-    for rec in filtered:
-        if "Title:" in rec:
-            try:
-                title = rec.split("Title:")[1].split(",")[0].strip()
-                recommendations.append(f"{title}")
-            except:
-                recommendations.append(f"{rec.strip()}")
-                
-    recommendations = recommendations[:5]
-    
-    if not recommendations:
-        return " Sorry , I couldn't find movies for that genre."
-    return "\n".join(recommendations)
-#gradio ui
-
-interface = gr.Interface(
-    fn=recommend_movie,
-    inputs=gr.Textbox(
-        label="What type of movie are you in the mood for?",
-        placeholder="e.g. Action, Comedy, Drama, Sci-Fi"
-    ),
-    outputs=gr.Textbox(label="🍿 Movie Recommendations"),
-    title="🎥 MovieRecBot",
-    description="Movie recommendation system powered by LlamaIndex + TinyLlama (Hugging Face)",
-    examples=[["Action"], ["Comedy"], ["Romance"], ["Sci-Fi"]],
-)
-interface.launch(share=True)
-
+# !pip install --upgrade llama-index llama-index-embeddings-huggingface llama-index-llms-huggingface transformers torch accelerate bitsandbytes
+
+from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex, Document
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.huggingface import HuggingFaceLLM
+import torch
+import os
+import pandas as pd
+import gradio as gr
+
+Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="sentence-transformers/all-MiniLM-L6-v2")
+Settings.llm = HuggingFaceLLM(
+    model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    tokenizer_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    context_window=2048,
+    max_new_tokens=256,
+    device_map="auto",
+    model_kwargs={"torch_dtype": torch.float16}
+)
+
+
+csv_file_path = "movie_recommendations_with_names.csv"
+df = None
+
+try:
+    df = pd.read_csv(csv_file_path)
+except FileNotFoundError:
+    print(f"Error: CSV file not found at {csv_file_path}")
+    print(f"Current working directory: {os.getcwd()}")
+
+except Exception as e:
+    print(f"An unexpected error occurred while reading the CSV: {e}")
+    
+if df is not None:
+    movies_data = [
+        Document(text=f"MovieID: {row['movie_id']}, Title: {row['title']}, Genre: {row['genre']}, Rating: {row['rating']}",
+                 metadata={"movie_id": row['movie_id'], "title": row['title'], "genre": row['genre'], "rating": row['rating']})
+        for index, row in df.iterrows()
+    ]
+index = VectorStoreIndex.from_documents(movies_data)
+query_engine = index.as_query_engine()
+
+def recommend_movie(genre):
+    if not genre.strip():
+        return "! Please enter a movie genre."
+    
+    response = query_engine.query(
+        f"List titles and genre of movies with genre {genre}."
+        f"provide at least 3 recommendations if avalabile."
+    )
+    
+    response_lines= str(response).split("\n")
+    filtered = [
+        line for line in response_lines
+        if line.strip()
+        and "Note : The query is not specific" not in line
+        ]
+    
+    recommendations = []
+    for rec in filtered:
+        if "Title:" in rec:
+            try:
+                title = rec.split("Title:")[1].split(",")[0].strip()
+                recommendations.append(f"{title}")
+            except:
+                recommendations.append(f"{rec.strip()}")
+                
+    recommendations = recommendations[:5]
+    
+    if not recommendations:
+        return " Sorry , I couldn't find movies for that genre."
+    return "\n".join(recommendations)
+#gradio ui
+
+interface = gr.Interface(
+    fn=recommend_movie,
+    inputs=gr.Textbox(
+        label="What type of movie are you in the mood for?",
+        placeholder="e.g. Action, Comedy, Drama, Sci-Fi"
+    ),
+    outputs=gr.Textbox(label="🍿 Movie Recommendations"),
+    title="🎥 MovieRecBot",
+    description="Movie recommendation system powered by LlamaIndex + TinyLlama (Hugging Face)",
+    examples=[["Action"], ["Comedy"], ["Romance"], ["Sci-Fi"]],
+)
+interface.launch(share=True)
+