Update app.py

app.py

@@ -11,22 +11,21 @@ from datetime import datetime
 # Authenticate Hugging Face token securely
 login(token=os.getenv("HF_TOKEN"))
 
-# ✅ Load FAISS index and product text
+# Load FAISS index and product data
 index = faiss.read_index("deberta_faiss.index")
 text_data = pd.read_csv("deberta_text_data.csv")["Retrieved Text"].tolist()
 
-# ✅ Load DeBERTa model for embeddings
+# Load DeBERTa for embedding
 deberta_model_name = "microsoft/deberta-v3-base"
 deberta_tokenizer = AutoTokenizer.from_pretrained(deberta_model_name)
 deberta_model = AutoModel.from_pretrained(deberta_model_name).to("cpu")
 
-# ✅ Load Falcon 1B model for generation (fast + public)
+# Load Falcon 1B for text generation
 llm_model_name = "tiiuae/falcon-rw-1b"
 llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
 llm_model = AutoModelForCausalLM.from_pretrained(llm_model_name)
 llm_pipeline = pipeline("text-generation", model=llm_model, tokenizer=llm_tokenizer, device=-1)
 
-# ✅ Log file
 log_path = "query_logs.txt"
 
 def generate_embeddings(queries):
@@ -36,18 +35,15 @@ def generate_embeddings(queries):
     return embeddings
 
 def generate_response(user_query):
-    # 🔹 Log the user query
     with open(log_path, "a") as log_file:
         log_file.write(f"{datetime.now()} - {user_query}\n")
 
-    # 🔹 Embed and retrieve
     query_embedding = generate_embeddings([user_query])
     faiss.normalize_L2(query_embedding)
     distances, indices = index.search(query_embedding, k=5)
     retrieved_docs = [text_data[i] for i in indices[0]]
     context = ", ".join(set(retrieved_docs))
 
-    # 🔹 Prompt
     prompt = f"""
     Using the following product descriptions:
     {context}
@@ -65,11 +61,10 @@ def generate_response(user_query):
     **Your response:**
     """
 
-    # 🔹 Generate response
-    result = llm_pipeline(prompt, max_length=512, do_sample=True, truncation=True)[0]["generated_text"]
+    result = llm_pipeline(prompt, max_new_tokens=300, do_sample=True, truncation=True, pad_token_id=llm_tokenizer.eos_token_id)[0]["generated_text"]
     return result
 
-# ✅ Gradio Blocks UI with logo, branding
+# Gradio UI with branding
 with gr.Blocks(css="""
     .logo-container {
         display: flex;
@@ -77,25 +72,23 @@ with gr.Blocks(css="""
         margin-bottom: -40px;
     }
     .logo-container img {
-        max-width: 300px;
+        max-width: 280px;
         height: auto;
-        border-radius: 10px;
+        border-radius: 8px;
     }
-""") as demo:
+""", theme=gr.themes.Soft()) as demo:
+    
     with gr.Column():
         with gr.Row():
-            gr.HTML("<div class='logo-container'><img src='OnlineRetail.png'></div>")
+            gr.HTML("<div class='logo-container'><img src='OnlineRetail.png' alt='Luxury Logo'></div>")
         gr.Markdown("### 🛋️ Luxury Decor Assistant")
-        gr.Markdown("Ask your decor questions based on real product descriptions. Powered by DeBERTa + FAISS + Falcon 1B.")
+        gr.Markdown("Ask your decor questions based on real product descriptions. <br> Powered by **DeBERTa + FAISS + Falcon 1B**.")
 
         with gr.Row():
             input_box = gr.Textbox(label="Textbox", placeholder="e.g. Suggest cozy decor for a small bedroom")
             output_box = gr.Textbox(label="Response")
 
         submit_btn = gr.Button("✨ Generate Suggestion")
-
         submit_btn.click(fn=generate_response, inputs=input_box, outputs=output_box)
 
-
-# ✅ Launch the app
-demo.launch()
+demo.launch()