Update app.py

app.py

@@ -1,19 +1,17 @@
 import os
 import gradio as gr
 import pandas as pd
-import numpy as np
 import faiss
 import torch
-from datetime import datetime
 from transformers import AutoTokenizer, AutoModel
-from openai import OpenAI, OpenAIError
+from openai import OpenAI
+from huggingface_hub import login
 
-# ✅ 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 environment secrets securely
+login(token=os.getenv("HF_TOKEN"))  # Hugging Face login
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # OpenAI GPT-3.5 auth
 
-# ✅ Load FAISS index and product data
+# ✅ Load FAISS index and product descriptions
 index = faiss.read_index("deberta_faiss.index")
 text_data = pd.read_csv("deberta_text_data.csv")["Retrieved Text"].tolist()
 
@@ -22,75 +20,71 @@ 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")
 
-# ✅ Helper: Generate embedding from DeBERTa
-def generate_embeddings(queries):
-    tokens = deberta_tokenizer(queries, return_tensors="pt", padding=True, truncation=True).to("cpu")
+# ✅ Embedding function
+def generate_embeddings(texts):
+    tokens = deberta_tokenizer(texts, return_tensors="pt", padding=True, truncation=True).to("cpu")
     with torch.no_grad():
         embeddings = deberta_model(**tokens).last_hidden_state.mean(dim=1).cpu().numpy().astype("float32")
     return embeddings
 
-# ✅ 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])
+# ✅ RAG-based response using OpenAI
+def generate_response(message, history):
+    # Embed and retrieve context
+    query_embedding = generate_embeddings([message])
     faiss.normalize_L2(query_embedding)
-    distances, indices = index.search(query_embedding, k=5)
+    _, indices = index.search(query_embedding, k=5)
     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)}"
+    context = "\n- " + "\n- ".join(set(retrieved_docs))
+
+    # Format history for OpenAI
+    chat_history = [{"role": "system", "content": "You're a helpful luxury interior design assistant."}]
+    for user, bot in history:
+        chat_history.append({"role": "user", "content": user})
+        chat_history.append({"role": "assistant", "content": bot})
+
+    # Append latest query
+    chat_history.append({
+        "role": "user",
+        "content": f"Here are related product descriptions:{context}\n\nUser Question: {message}\n\nAnswer:"
+    })
+
+    # Call OpenAI GPT-3.5
+    completion = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=chat_history,
+        temperature=0.7,
+        max_tokens=500
+    )
+    response = completion.choices[0].message.content.strip()
+
+    # Generate a follow-up question
+    followup_prompt = f"Based on this interior decor answer: '{response}', suggest a helpful follow-up question the user might ask next."
+    followup_completion = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": followup_prompt}],
+        temperature=0.7,
+        max_tokens=60
+    )
+    followup = followup_completion.choices[0].message.content.strip()
 
-    return reply
+    final_output = f"🪑 **Decor Response:** {response}\n\n🔄 **Suggested Follow-Up:** {followup}"
+    return final_output
 
 # ✅ Gradio Chat UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     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."
-    )
+    gr.Markdown("💬 Ask your interior design questions using real product descriptions. Powered by **DeBERTa + FAISS + OpenAI GPT-3.5**.")
 
-    chatbot = gr.Chatbot(label="Chatbot", height=400)
-    msg = gr.Textbox(label="Textbox", placeholder="e.g. Suggest cozy decor for Neha Study room")
+    chatbot = gr.Chatbot(label="Chatbot")
+    msg = gr.Textbox(label="Textbox", placeholder="e.g. Suggest cozy decor for a small bedroom", scale=8)
     clear = gr.Button("Clear")
 
-    def respond(message, history):
-        if history is None:
-            history = []
-        response = generate_response(message, history)
-        history.append((message, response))
-        return history, ""
+    def respond(message, chat_history):
+        response = generate_response(message, chat_history)
+        chat_history.append((message, response))
+        return "", chat_history
 
-    msg.submit(respond, [msg, chatbot], [chatbot, msg])
-    clear.click(lambda: ([], ""), None, [chatbot, msg])
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
 
-# ✅ Launch the app
 demo.launch()