Create app.py

app.py

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+import os
+import sys
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from sentence_transformers import SentenceTransformer
+import torch
+
+# sqlite workaround for HuggingFace Spaces
+__import__('pysqlite3')
+sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+
+# Load documents
+docs = []
+for f in os.listdir("multiple_docs"):
+    if f.endswith(".pdf"):
+        loader = PyPDFLoader(os.path.join("multiple_docs", f))
+        docs.extend(loader.load())
+    elif f.endswith(".docx") or f.endswith(".doc"):
+        loader = Docx2txtLoader(os.path.join("multiple_docs", f))
+        docs.extend(loader.load())
+    elif f.endswith(".txt"):
+        loader = TextLoader(os.path.join("multiple_docs", f))
+        docs.extend(loader.load())
+
+# Split docs
+splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
+docs = splitter.split_documents(docs)
+
+# Embeddings
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+texts = [doc.page_content for doc in docs]
+metadatas = [{"id": i} for i in range(len(texts))]
+embeddings = embedding_model.encode(texts)
+
+# Vectorstore
+vectorstore = Chroma(persist_directory="./db")
+vectorstore.add_texts(texts=texts, metadatas=metadatas, embeddings=embeddings)
+vectorstore.persist()
+
+
+model_name = "deepseek-ai/deepseek-llm-7b-instruct"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+def generate(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+class HuggingFaceLLMWrapper:
+    def __call__(self, prompt, **kwargs):
+        return generate(prompt)
+
+llm = HuggingFaceLLMWrapper()
+
+# QA chain
+chain = ConversationalRetrievalChain.from_llm(
+    llm,
+    retriever=vectorstore.as_retriever(search_kwargs={'k': 6}),
+    return_source_documents=True,
+    verbose=False
+)
+
+chat_history = []
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot([("", "Hello, I'm Thierry Decae's chatbot. Ask me about my experience, skills, eligibility, etc.")],
+                         avatar_images=["./multiple_docs/Guest.jpg", "./multiple_docs/Thierry Picture.jpg"])
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def user(query, chat_history):
+        chat_history_tuples = [(m[0], m[1]) for m in chat_history]
+        result = chain({"question": query, "chat_history": chat_history_tuples})
+        chat_history.append((query, result["answer"]))
+        return gr.update(value=""), chat_history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch(debug=True)