Create app.py

app.py

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+import os
+import logging
+import torch
+import gradio as gr
+from tqdm import tqdm
+from PIL import Image
+
+# LangChain & LangGraph
+from langgraph.graph import StateGraph
+from langgraph.checkpoint.memory import MemorySaver
+from langchain.tools import tool
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from duckduckgo_search import DDGS
+from llama_cpp import Llama
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ------------------------------
+# 🔹 Load GGUF Model with llama-cpp-python
+# ------------------------------
+MODEL_PATH = "Bio-Medical-MultiModal-Llama-3-8B-V1.i1-Q4_0.gguf"
+
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(f"Model file {MODEL_PATH} not found. Upload it to the same directory.")
+
+llm = Llama(
+    model_path=MODEL_PATH,
+    n_ctx=8192,
+    n_gpu_layers=0,  # Set to 0 for CPU inference
+    logits_all=True,
+    n_batch=512
+)
+
+logger.info("Llama GGUF Model Loaded Successfully.")
+
+# ------------------------------
+# 🔹 Multi-Specialty Prompt
+# ------------------------------
+UNIFIED_MEDICAL_PROMPT = """
+You are an advanced Medical AI Assistant capable of providing thorough,
+comprehensive answers for a wide range of medical specialties:
+General Practice, Radiology, Cardiology, Neurology, Psychiatry, Pediatrics,
+Endocrinology, Oncology, and more.
+
+You can:
+1) Analyze images if provided (Radiology).
+2) Search the web for up-to-date medical info (Web Search).
+3) Retrieve relevant documents from a knowledge base (Vector Store).
+4) Provide scientific, evidence-based explanations and references when possible.
+
+Always strive to provide a detailed, helpful, and empathetic response.
+"""
+
+# ------------------------------
+# 🔹 FAISS Vector Store for RAG
+# ------------------------------
+_vector_store_cache = None
+
+def load_vectorstore(pdf_path="medical_docs.pdf"):
+    try:
+        loader = PyPDFLoader(pdf_path)
+        documents = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=100)
+        docs = text_splitter.split_documents(documents)
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        vector_store = FAISS.from_documents(docs, embeddings)
+        logger.info(f"Vector store loaded with {len(docs)} documents.")
+        return vector_store
+    except Exception as e:
+        logger.error(f"Error loading vector store: {str(e)}")
+        return None
+
+if os.path.exists("medical_docs.pdf"):
+    _vector_store_cache = load_vectorstore("medical_docs.pdf")
+
+vector_store = _vector_store_cache
+
+# ------------------------------
+# 🔹 Define AI Tools
+# ------------------------------
+@tool
+def analyze_medical_image(image_path: str):
+    """Analyzes a medical image and returns a diagnostic explanation."""
+    try:
+        image = Image.open(image_path)
+    except Exception as e:
+        logger.error(f"Error opening image: {str(e)}")
+        return "Error processing image."
+    output = llm(f"Analyze this medical image for radiological findings:\n{image}", max_tokens=512)
+    return output["choices"][0]["text"]
+
+@tool
+def retrieve_medical_knowledge(query: str):
+    """Retrieves medical knowledge from FAISS vector store."""
+    if vector_store is None:
+        return "No external medical knowledge available."
+    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+    docs = retriever.get_relevant_documents(query)
+    citations = [f"[{i+1}] {doc.metadata.get('source', 'Unknown Source')}" for i, doc in enumerate(docs)]
+    content = "\n".join([doc.page_content for doc in docs])
+    citations_text = "\n".join(citations)
+    return content + f"\n\n**Citations:**\n{citations_text}"
+
+@tool
+def web_search(query: str):
+    """Performs a real-time web search using DuckDuckGo."""
+    try:
+        results = DDGS().text(query, max_results=5)
+        summary = "\n".join([f"{r['title']}: {r['body']} ({r['href']})" for r in results]) or "No relevant results found."
+        return summary
+    except Exception as e:
+        logger.error(f"Web search error: {str(e)}")
+        return "Error retrieving web search results."
+
+# ------------------------------
+# 🔹 Multi-Context Chat Function
+# ------------------------------
+def chat_with_agent(user_query, image_file, pdf_file):
+    image_analysis = analyze_medical_image(image_file) if image_file else ""
+    rag_text = retrieve_medical_knowledge(user_query)
+    web_text = web_search(user_query)
+
+    combined_context = f"""
+    {UNIFIED_MEDICAL_PROMPT}
+
+    Additional Context:
+    - Radiology Analysis (if any): {image_analysis}
+    - Retrieved from Vector Store (RAG): {rag_text}
+    - Web Search Results: {web_text}
+
+    Now, respond to the user's query with detailed, medically accurate information.
+    Q: {user_query}
+    A:
+    """
+
+    response_accumulator = ""
+    for token in llm(
+        prompt=combined_context,
+        max_tokens=1024,
+        temperature=0.7,
+        top_p=0.9,
+        stream=True
+    ):
+        partial_text = token["choices"][0]["text"]
+        response_accumulator += partial_text
+        yield response_accumulator
+
+# ------------------------------
+# 🔹 Gradio Interface
+# ------------------------------
+with gr.Blocks(title="🏥 Llama3-Med AI Assistant") as demo:
+    gr.Markdown("# 🏥 Llama3-Med AI Assistant\n_Your intelligent medical assistant powered by advanced AI._")
+
+    with gr.Row():
+        user_input = gr.Textbox(label="💬 Ask a medical question", placeholder="Type your question here...")
+        image_file = gr.Image(label="📷 Upload Medical Image", type="filepath")
+        pdf_file = gr.File(label="📄 Upload PDF (Optional)", file_types=[".pdf"])
+
+    submit_btn = gr.Button("🚀 Submit", variant="primary")
+    output_text = gr.Textbox(label="📝 Assistant's Response", interactive=False, lines=25)
+
+    submit_btn.click(fn=chat_with_agent, inputs=[user_input, image_file, pdf_file], outputs=output_text)
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)