Update app.py

app.py

@@ -12,7 +12,7 @@ sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
 processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 image_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
 
-def generate_input(input_type, image=None, text=None):
+def generate_input(input_type, image=None, text=None, response_amount=3):
     # Initialize the input variable
     combined_input = ""
 
@@ -38,49 +38,61 @@ def generate_input(input_type, image=None, text=None):
     if not combined_input:
         combined_input = "No input provided."
 
-    return vector_search(combined_input)
+    return vector_search(combined_input,response_amount)
 
 # Load embeddings and metadata
 embeddings = np.load("netflix_embeddings.npy")  #created using sentence_transformers on kaggle
 metadata = pd.read_csv("netflix_metadata.csv") #created using sentence_transformers on kaggle
 
 # Vector search function
-def vector_search(query):
+def vector_search(query,top_n=3):
     query_embedding = sentence_model.encode(query)
     similarities = cosine_similarity([query_embedding], embeddings)[0]
-    top_n = 3
     top_indices = similarities.argsort()[-top_n:][::-1]
     results = metadata.iloc[top_indices]
-    
-    # Format results for display
-    result_text = "\n".join(f"Title: {row['title']}, Description: {row['description']}, Genre: {row['listed_in']}" for _, row in results.iterrows())
+    result_text=""
+    for index,row in results.iterrows():
+        if index!=top_n-1:
+            result_text+=f"Title:  {row['title']}  Description: {row['description']}  Genre: {row['listed_in']}\n\n"
+        else:
+            result_text+=f"Title:  {row['title']}  Description: {row['description']}  Genre: {row['listed_in']}"
     return result_text
+
+
+def set_response_amount(response_amount):
+    return response_amount
+
+ # Based on the selected input type, make the appropriate input visible
+def update_inputs(input_type):
+    if input_type == "Image":
+        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)
+    elif input_type == "Text":
+        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)
+    elif input_type == "Both":
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
 with gr.Blocks() as demo:
     gr.Markdown("# Netflix Recommendation System")
     gr.Markdown("Enter a query to receive Netflix show recommendations based on title, description, and genre.")
    
     input_type = gr.Radio(["Image", "Text", "Both"], label="Select Input Type", type="value")
+    response_type=gr.Dropdown(choices=[3,5,10,25], type="value", label="Select Response Amount", visible=False)
     image_input = gr.Image(label="Upload Image", type="pil", visible=False)  # Hidden initially
     text_input = gr.Textbox(label="Enter Text Query", placeholder="Enter a description or query here", visible=False)  # Hidden initially
-    
-    # Based on the selected input type, make the appropriate input visible
-    def update_inputs(input_type):
-        if input_type == "Image":
-            return gr.update(visible=True), gr.update(visible=False)
-        elif input_type == "Text":
-            return gr.update(visible=False), gr.update(visible=True)
-        elif input_type == "Both":
-            return gr.update(visible=True), gr.update(visible=True)
-    
-    input_type.change(fn=update_inputs, inputs=input_type, outputs=[image_input, text_input])
+  
+    input_type.change(fn=update_inputs, inputs=input_type, outputs=[image_input, text_input, response_type])
+   # State variable to store the selected response amount
+    selected_response_amount = gr.State()
+
+    # Capture response amount immediately when dropdown changes
+    response_type.change(fn=set_response_amount, inputs=response_type, outputs=selected_response_amount)
     
     submit_button = gr.Button("Submit")
     output = gr.Textbox(label="Recommendations")
 
-    submit_button.click(fn=generate_input, inputs=[input_type,image_input, text_input], outputs=output)
-
+    submit_button.click(fn=generate_input, inputs=[input_type,image_input, text_input,selected_response_amount], outputs=output)
 demo.launch()
 
+
 # with gr.Blocks() as demo:
 #     gr.Markdown("# Netflix Recommendation System")
 #     gr.Markdown("Enter a query to receive Netflix show recommendations based on title, description, and genre.")