Create app.py

app.py

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+import os
+import logging
+import gradio as gr
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ------------------------------
+# 🔹 Load Bioformer-8L Model
+# ------------------------------
+BIOFORMER_MODEL = "bioformers/bioformer-8L"
+bioformer_tokenizer = AutoTokenizer.from_pretrained(BIOFORMER_MODEL)
+bioformer_model = AutoModelForCausalLM.from_pretrained(BIOFORMER_MODEL)
+
+# ------------------------------
+# 🔹 Load DeepSeek-R1-Distill-Qwen-7B-GGUF Model
+# ------------------------------
+DEEPSEEK_REPO = "lmstudio-community/DeepSeek-R1-Distill-Qwen-7B-GGUF"
+DEEPSEEK_FILENAME = "DeepSeek-R1-Distill-Qwen-7B-Q4_0.gguf"
+
+model_path = hf_hub_download(repo_id=DEEPSEEK_REPO, filename=DEEPSEEK_FILENAME)
+
+llm = Llama(
+    model_path=model_path,
+    n_ctx=4096,
+    n_gpu_layers=0,  # CPU inference
+    logits_all=True,
+    n_batch=256
+)
+
+logger.info("Models Loaded Successfully.")
+
+# ------------------------------
+# 🔹 Unified Medical 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) Retrieve relevant documents from a knowledge base (Vector Store).
+3) Provide scientific, evidence-based explanations and references when possible.
+
+Always strive to provide a detailed, helpful, and empathetic response.
+"""
+
+# ------------------------------
+# 🔹 Chat Function
+# ------------------------------
+def chat_with_agent(user_query, image_file=None):
+    # Combine context
+    combined_context = f"""
+    {UNIFIED_MEDICAL_PROMPT}
+
+    Patient Query: "{user_query}"
+    Your Response:
+    """
+
+    # Generate response using DeepSeek-R1-Distill model
+    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
+    _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 (Optional)", type="filepath")
+
+    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], outputs=output_text)
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)