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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Medical_Book.pdf filter=lfs diff=lfs merge=lfs -text

Medical_Book.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:753cd53b7a3020bbd91f05629b0e3ddcfb6a114d7bbedb22c2298b66f5dd00cc
+size 16127037

README.md

@@ -1,13 +1,10 @@
----
-title: Medical Chatbot Space
-emoji: 📊
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
-short_description: A medical assistant chatbot
----
+# 🩺 Medical Chatbot using Mistral LLM
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+A medical assistant chatbot using LangChain, FAISS, HuggingFace LoRA model, and Gradio.
+
+## Features
+- Retrieval-Augmented Generation (RAG)
+- Conversational context
+- PDF ingestion (Medical_Book.pdf)
+
+Built and deployed by Mamadou Saidou Baldé.

app.py

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+import os
+import torch
+import gradio as gr
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import ConversationalRetrievalChain
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.llm import LLMChain
+
+# Load PDF
+loader = PyPDFLoader("Medical_Book.pdf")
+documents = loader.load()
+
+# Split text
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
+all_splits = text_splitter.split_documents(documents)
+
+# Embeddings
+model_name = "sentence-transformers/all-mpnet-base-v2"
+embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={"device": "cpu"})
+vectorstores = FAISS.from_documents(all_splits, embeddings)
+
+# Load LLM with quantization
+llm_model = "ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel"
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(llm_model, quantization_config=bnb_config, trust_remote_code=True, use_cache=True, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(llm_model, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=150, temperature=0.7, top_p=0.9, do_sample=True)
+llm = HuggingFacePipeline(pipeline=pipe)
+
+# Prompt templates
+condense_prompt = PromptTemplate(
+    input_variables=["question", "chat_history"],
+    template="""
+You are a helpful medical assistant. Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question.
+
+Chat History:
+{chat_history}
+
+Follow-up Question:
+{question}
+
+Standalone Question:"""
+)
+qa_prompt = PromptTemplate(
+    input_variables=["context", "question"],
+    template="""
+Use the following context to answer the question.
+
+Context:
+{context}
+
+Question:
+{question}
+
+Answer:
+"""
+)
+question_generator = LLMChain(llm=llm, prompt=condense_prompt)
+combine_docs_chain = load_qa_chain(llm=llm, chain_type="stuff", prompt=qa_prompt)
+
+def chatbot_response(user_input, max_new_tokens, temperature, context_length): 
+    pipe.model.config.max_new_tokens = int(max_new_tokens)
+    pipe.model.config.temperature = float(temperature)
+    pipe.model.config.context_length = int(context_length)
+    chain = ConversationalRetrievalChain(retriever=vectorstores.as_retriever(), combine_docs_chain=combine_docs_chain, question_generator=question_generator, return_source_documents=True)
+    chat_history = []
+    result = chain({"question": user_input, "chat_history": chat_history})
+    return f"<div style='max-height: 400px; overflow-y: auto;'>{result['answer']}</div>"
+
+interface = gr.Interface(
+    fn=chatbot_response,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Type your question here...", label="Your Question", interactive=True),
+        gr.Slider(label="Max New Tokens", minimum=1, maximum=2000, value=150, step=1, interactive=True),
+        gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7, step=0.01, interactive=True),
+        gr.Slider(label="Context Length", minimum=100, maximum=4000, value=2000, step=1, interactive=True)
+    ],
+    outputs=gr.HTML(label="Chatbot Response"),
+    title="🩺 MEDICAL Chatbot",
+    description="""
+    <div style='text-align: center;'>
+        <img src='https://cdn.dribbble.com/users/29678/screenshots/2407580/media/34ee4b818fd4ddb3a616c91ccf4d9cfc.png' alt='Medical Bot' width='100'>
+        <p>Check the responses from the Medical Llama 3-8B model!</p>
+    </div>
+    """
+)
+
+interface.launch()

requirements.txt

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+gradio
+transformers
+torch
+langchain
+langchain_community
+langchain-huggingface
+sentence-transformers
+pypdf
+faiss-cpu
+bitsandbytes
+accelerate
+scikit-learn
+typer==0.10.0