Update model.py

model.py

@@ -1,128 +1,130 @@
-from langchain.prompts import PromptTemplate
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.llms import HuggingFacePipeline
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-from langchain.chains import RetrievalQA
-import chainlit as cl
-from dotenv import load_dotenv
-import torch
-import os
-
-load_dotenv()  
-
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-# Prompt Template
-custom_prompt_template = """Use the following pieces of information to answer the user's question.
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-Context: {context}
-Question: {question}
-
-Only return the helpful answer below and nothing else.
-Helpful answer:
-"""
-
-def set_custom_prompt():
-    prompt = PromptTemplate(template=custom_prompt_template,
-                            input_variables=['context', 'question'])
-    return prompt
-
-# Create RetrievalQA chain
-def retrieval_qa_chain(llm, prompt, db):
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type='stuff',
-        retriever=db.as_retriever(search_kwargs={'k': 2}),
-        return_source_documents=True,
-        chain_type_kwargs={'prompt': prompt}
-    )
-    return qa_chain
-
-# Load Hugging Face LLM
-def load_llm():
-    # Load model and tokenizer
-    tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
-    model = AutoModelForSeq2SeqLM.from_pretrained(
-        "google/flan-t5-base",
-        device_map="cpu",
-        torch_dtype=torch.float32
-    )
-
-    # Create text-generation pipeline without invalid parameters
-    pipe = pipeline(
-        "text2text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        repetition_penalty=1.15
-    )
-
-    # Create LangChain wrapper for the pipeline
-    llm = HuggingFacePipeline(pipeline=pipe)
-    return llm
-
-# Build full chatbot pipeline
-def qa_bot():
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",
-        model_kwargs={'device': 'cpu'}
-    )
-    db = FAISS.load_local(
-        DB_FAISS_PATH,
-        embeddings,
-        allow_dangerous_deserialization=True
-    )
-
-    llm = load_llm()
-    qa_prompt = set_custom_prompt()
-    qa = retrieval_qa_chain(llm, qa_prompt, db)
-    return qa
-
-# Run for one query (used internally)
-def final_result(query):
-    qa_result = qa_bot()
-    response = qa_result({'query': query})
-    return response
-
-# Chainlit UI - Start
-@cl.on_chat_start
-async def start():
-    chain = qa_bot()
-    msg = cl.Message(content="Starting the bot...")
-    await msg.send()
-    msg.content = "Hi, Welcome to MindMate. What is your query?"
-    await msg.update()
-    cl.user_session.set("chain", chain)
-
-# Chainlit UI - Handle messages
-@cl.on_message
-async def main(message: cl.Message):
-    chain = cl.user_session.get("chain")
-    cb = cl.AsyncLangchainCallbackHandler(
-        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
-    )
-    cb.answer_reached = True
-    
-    # Use invoke with proper query format
-    res = await cl.make_async(chain.invoke)(
-        {"query": message.content}, 
-        callbacks=[cb]
-    )
-    
-    # Extract result and sources from the response
-    answer = res.get("result", "No result found")
-    sources = res.get("source_documents", [])
-
-    # Format sources to show only the content
-    if sources:
-        formatted_sources = []
-        for source in sources:
-            if hasattr(source, 'page_content'):
-                formatted_sources.append(source.page_content.strip())
-        
-        if formatted_sources:
-            answer = f"{answer}\n\nBased on the following information:\n" + "\n\n".join(formatted_sources)
-
-    await cl.Message(content=answer).send()
+from langchain.prompts import PromptTemplate
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.llms import HuggingFacePipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from langchain.chains import RetrievalQA
+from download_assets import download_assets
+download_assets()  
+import chainlit as cl
+from dotenv import load_dotenv
+import torch
+import os
+
+load_dotenv()  
+
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+# Prompt Template
+custom_prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+def set_custom_prompt():
+    prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+    return prompt
+
+# Create RetrievalQA chain
+def retrieval_qa_chain(llm, prompt, db):
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type='stuff',
+        retriever=db.as_retriever(search_kwargs={'k': 2}),
+        return_source_documents=True,
+        chain_type_kwargs={'prompt': prompt}
+    )
+    return qa_chain
+
+# Load Hugging Face LLM
+def load_llm():
+    # Load model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
+    model = AutoModelForSeq2SeqLM.from_pretrained(
+        "google/flan-t5-base",
+        device_map="cpu",
+        torch_dtype=torch.float32
+    )
+
+    # Create text-generation pipeline without invalid parameters
+    pipe = pipeline(
+        "text2text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        max_new_tokens=512,
+        repetition_penalty=1.15
+    )
+
+    # Create LangChain wrapper for the pipeline
+    llm = HuggingFacePipeline(pipeline=pipe)
+    return llm
+
+# Build full chatbot pipeline
+def qa_bot():
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        model_kwargs={'device': 'cpu'}
+    )
+    db = FAISS.load_local(
+        DB_FAISS_PATH,
+        embeddings,
+        allow_dangerous_deserialization=True
+    )
+
+    llm = load_llm()
+    qa_prompt = set_custom_prompt()
+    qa = retrieval_qa_chain(llm, qa_prompt, db)
+    return qa
+
+# Run for one query (used internally)
+def final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'query': query})
+    return response
+
+# Chainlit UI - Start
+@cl.on_chat_start
+async def start():
+    chain = qa_bot()
+    msg = cl.Message(content="Starting the bot...")
+    await msg.send()
+    msg.content = "Hi, Welcome to MindMate. What is your query?"
+    await msg.update()
+    cl.user_session.set("chain", chain)
+
+# Chainlit UI - Handle messages
+@cl.on_message
+async def main(message: cl.Message):
+    chain = cl.user_session.get("chain")
+    cb = cl.AsyncLangchainCallbackHandler(
+        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
+    )
+    cb.answer_reached = True
+    
+    # Use invoke with proper query format
+    res = await cl.make_async(chain.invoke)(
+        {"query": message.content}, 
+        callbacks=[cb]
+    )
+    
+    # Extract result and sources from the response
+    answer = res.get("result", "No result found")
+    sources = res.get("source_documents", [])
+
+    # Format sources to show only the content
+    if sources:
+        formatted_sources = []
+        for source in sources:
+            if hasattr(source, 'page_content'):
+                formatted_sources.append(source.page_content.strip())
+        
+        if formatted_sources:
+            answer = f"{answer}\n\nBased on the following information:\n" + "\n\n".join(formatted_sources)
+
+    await cl.Message(content=answer).send()