Update main.py

main.py

@@ -1,82 +1,101 @@
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_community.document_loaders import TextLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_community.vectorstores import FAISS
-from dotenv import load_dotenv
-
-load_dotenv()
-
-llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
-
-embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004")
-
-# --- Load products ---
-loader = TextLoader("products.json")
-docs = loader.load()
-splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-text = splitter.split_documents(docs)
-product_store = FAISS.from_documents(documents=text, embedding=embeddings)
-
-# --- Load FAQs ---
-loader = TextLoader("faqs.json")
-docs = loader.load()
-splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-text = splitter.split_documents(docs)
-faq_store = FAISS.from_documents(documents=text, embedding=embeddings)
-
-# --- Retrievers ---
-product_retriever = product_store.as_retriever(search_kwargs={"k": 3})
-faq_retriever = faq_store.as_retriever(search_kwargs={"k": 3})
-
-# --- Keywords ---
-FAQ_KEYWORDS = {
-    "delivery", "ship", "shipping", "return", "refund", "warranty",
-    "payment", "pay", "exchange", "order", "track", "policy"
-}
-PRODUCT_KEYWORDS = {
-    "price", "spec", "specs", "specifications", "feature", "features",
-    "compare", "details", "model", "brand", "laptop", "mobile",
-    "phone", "shoes", "camera", "ram", "ssd", "storage", "gpu", "cpu"
-}
-
-# --- Conversation history ---
-conversation_history = []
-
-# --- Functions ---
-def get_relevant_retriever(query: str):
-    q = query.lower()
-    if any(word in q for word in FAQ_KEYWORDS):
-        return faq_retriever
-    elif any(word in q for word in PRODUCT_KEYWORDS):
-        return product_retriever
-    else:
-        return product_retriever  # default
-
-def ask_bot(query: str):
-    retriever = get_relevant_retriever(query)
-    docs = retriever.get_relevant_documents(query)
-    context = "\n".join([d.page_content for d in docs])
-
-    # --- Add previous conversation history to context ---
-    history_text = ""
-    for turn in conversation_history[-6:]:  # last 3 user-bot pairs
-        history_text += f"User: {turn['user']}\nBot: {turn['bot']}\n"
-
-    full_prompt = f"{history_text}\nContext:\n{context}\n\nUser question: {query}\nAnswer:"
-    response = llm.invoke(full_prompt)
-
-    # --- Save this turn to history ---
-    conversation_history.append({"user": query, "bot": response.content})
-
-    return response.content
-
-# --- Main Loop ---
-if __name__ == "__main__":
-    print("Chatbot started. Type 'exit' or 'quit' to stop.\n")
-    while True:
-        query = input("You: ")
-        if query.lower() in ["exit", "quit", "q"]:
-            break
-        answer = ask_bot(query)
-        print("Bot:", answer)
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from dotenv import load_dotenv
+
+load_dotenv()
+
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
+
+embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004")
+
+# --- Load products ---
+loader = TextLoader("products.json")
+docs = loader.load()
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+text = splitter.split_documents(docs)
+product_store = FAISS.from_documents(documents=text, embedding=embeddings)
+
+# --- Load FAQs ---
+loader = TextLoader("faqs.json")
+docs = loader.load()
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+text = splitter.split_documents(docs)
+faq_store = FAISS.from_documents(documents=text, embedding=embeddings)
+
+# --- Retrievers ---
+product_retriever = product_store.as_retriever(search_kwargs={"k": 3})
+faq_retriever = faq_store.as_retriever(search_kwargs={"k": 3})
+
+# --- Keywords ---
+FAQ_KEYWORDS = {
+    "delivery", "ship", "shipping", "return", "refund", "warranty",
+    "payment", "pay", "exchange", "order", "track", "policy"
+}
+PRODUCT_KEYWORDS = {
+    "price", "spec", "specs", "specifications", "feature", "features",
+    "compare", "details", "model", "brand", "laptop", "mobile",
+    "phone", "shoes", "camera", "ram", "ssd", "storage", "gpu", "cpu"
+}
+
+# --- Conversation history ---
+conversation_history = []
+
+# --- Functions ---
+def get_relevant_retriever(query: str):
+    q = query.lower()
+    if any(word in q for word in FAQ_KEYWORDS):
+        return faq_retriever
+    elif any(word in q for word in PRODUCT_KEYWORDS):
+        return product_retriever
+    else:
+        return product_retriever  # default
+
+def ask_bot(query: str):
+    retriever = get_relevant_retriever(query)
+    docs = retriever.get_relevant_documents(query)
+    context = "\n".join([d.page_content for d in docs])
+
+    # --- Add previous conversation history to context ---
+    history_text = ""
+    for turn in conversation_history[-6:]:  # last 3 user-bot pairs
+        history_text += f"User: {turn['user']}\nBot: {turn['bot']}\n"
+
+    full_prompt = f"""You are ShopMart's helpful e-commerce assistant. 
+
+IMPORTANT RULES:
+- Answer the EXACT question asked by the user
+- Use ONLY the information provided in the context below
+- For product questions, show specific products with names and prices
+- If user asks about products "under X PKR", show products within that budget
+- Never give generic promotional responses or templates
+- Be specific and helpful, not vague
+- Include actual product details like prices, specs, availability
+
+{history_text}
+
+Context Information:
+{context}
+
+User question: {query}
+
+Helpful Answer:"""
+    
+    response = llm.invoke(full_prompt)
+
+    # --- Save this turn to history ---
+    conversation_history.append({"user": query, "bot": response.content})
+
+    return response.content
+
+# --- Main Loop ---
+if __name__ == "__main__":
+    print("Chatbot started. Type 'exit' or 'quit' to stop.\n")
+    while True:
+        query = input("You: ")
+        if query.lower() in ["exit", "quit", "q"]:
+            break
+        answer = ask_bot(query)
+        print("Bot:", answer)