Update app.py

app.py

@@ -1,91 +1,96 @@
 import os
 import gradio as gr
 import pandas as pd
+import numpy as np
 import faiss
 import torch
-from transformers import AutoTokenizer, AutoModel
-from openai import OpenAI
 from datetime import datetime
+from transformers import AutoTokenizer, AutoModel
+from openai import OpenAI, OpenAIError
 
-# ✅ Hugging Face secret & OpenAI token setup
+# ✅ Load Hugging Face and OpenAI credentials
 HF_TOKEN = os.getenv("HF_TOKEN")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 client = OpenAI(api_key=OPENAI_API_KEY)
 
-# ✅ Load DeBERTa model for embeddings
+# ✅ 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 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 FAISS index + data
-index = faiss.read_index("deberta_faiss.index")
-text_data = pd.read_csv("deberta_text_data.csv")["Retrieved Text"].tolist()
-
-# ✅ Embedding generator
+# ✅ Helper: Generate embedding from DeBERTa
 def generate_embeddings(queries):
-    inputs = deberta_tokenizer(queries, return_tensors="pt", padding=True, truncation=True).to("cpu")
+    tokens = deberta_tokenizer(queries, return_tensors="pt", padding=True, truncation=True).to("cpu")
     with torch.no_grad():
-        embeddings = deberta_model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy().astype("float32")
+        embeddings = deberta_model(**tokens).last_hidden_state.mean(dim=1).cpu().numpy().astype("float32")
     return embeddings
 
-# ✅ Query + context → prompt for GPT-3.5
-def generate_response(user_query, history):
-    # Embed the query and retrieve top documents
-    query_embedding = generate_embeddings([user_query])
+# ✅ Main logic: Compose RAG prompt and get GPT-3.5 response
+def generate_response(user_message, chat_history):
+    # Embed the query and retrieve docs
+    query_embedding = generate_embeddings([user_message])
     faiss.normalize_L2(query_embedding)
     distances, indices = index.search(query_embedding, k=5)
-    context_docs = [text_data[i] for i in indices[0]]
-    context = "\n".join(context_docs)
-
-    # Construct the chat history for OpenAI
-    messages = [{"role": "system", "content": "You are a helpful interior decor assistant. Use luxury home decor examples and retrieved documents to answer queries."}]
-    for user_msg, bot_reply in history:
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": bot_reply})
-
-    # Add the latest user query
-    messages.append({
-        "role": "user",
-        "content": f"Context:\n{context}\n\nUser Query:\n{user_query}\n\nGive a helpful, elegant, practical luxury decor suggestion."
-    })
-
-    # Call OpenAI GPT-3.5
-    response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=messages,
-        max_tokens=512,
-        temperature=0.7
-    )
-
-    decor_response = response.choices[0].message.content.strip()
-
-    # Follow-up question prompt
-    followup_prompt = f"Given the user's query: '{user_query}' and the answer: '{decor_response}', suggest a relevant follow-up decor question."
-    followup = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": followup_prompt}],
-        max_tokens=60
-    ).choices[0].message.content.strip()
-
-    full_reply = f"🪑 **Decor Response:** {decor_response}\n\n🔄 **Suggested Follow-Up:** {followup}"
-    return full_reply
-
-# ✅ Gradio chatbot interface
+    retrieved_docs = [text_data[i] for i in indices[0]]
+    context = "\n".join(set(retrieved_docs))
+
+    # Build system prompt with context
+    system_prompt = f"""
+You are a luxury home decor assistant. Use the product context below to answer questions about home interiors.
+
+Product Descriptions:
+{context}
+
+Always provide helpful, elegant, and context-aware interior design suggestions.
+Also suggest a follow-up question based on the user query to keep the conversation going.
+"""
+
+    messages = [{"role": "system", "content": system_prompt}]
+    
+    for message in chat_history:
+        messages.append({"role": "user", "content": message[0]})
+        messages.append({"role": "assistant", "content": message[1]})
+
+    messages.append({"role": "user", "content": user_message})
+
+    try:
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            temperature=0.7,
+            max_tokens=512
+        )
+        reply = response.choices[0].message.content.strip()
+    except OpenAIError as e:
+        reply = f"❌ Error: {str(e)}"
+
+    return reply
+
+# ✅ Gradio Chat UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("### 🛋️ **Luxury Decor Assistant (RAG)**\nAsk your interior design questions using real product descriptions. Powered by **DeBERTa + FAISS + Flan-T5** → now upgraded to **OpenAI GPT-3.5** for enhanced answers.")
+    gr.Markdown("## 🪑 Luxury Decor Assistant (RAG)")
+    gr.Markdown(
+        "💬 Ask your interior design questions using real product descriptions. "
+        "Powered by **DeBERTa + FAISS** → now upgraded to **OpenAI GPT-3.5** for enhanced answers."
+    )
 
-    chatbot = gr.Chatbot(label="Chatbot", show_label=True)
-    user_input = gr.Textbox(label="Textbox", placeholder="e.g. Suggest cozy decor for Neha Study room", show_label=True)
-    clear_btn = gr.Button("Clear")
+    chatbot = gr.Chatbot(label="Chatbot", height=400)
+    msg = gr.Textbox(label="Textbox", placeholder="e.g. Suggest cozy decor for Neha Study room")
+    clear = gr.Button("Clear")
 
     def respond(message, history):
         if history is None:
             history = []
-        reply = generate_response(message, history)
-        history.append((message, reply))
-        return history, history
+        response = generate_response(message, history)
+        history.append((message, response))
+        return history, ""
 
-    user_input.submit(respond, [user_input, chatbot], [chatbot, chatbot])
-    clear_btn.click(lambda: ([], ""), None, [chatbot, user_input])
+    msg.submit(respond, [msg, chatbot], [chatbot, msg])
+    clear.click(lambda: ([], ""), None, [chatbot, msg])
 
+# ✅ Launch the app
 demo.launch()