Create app.py

app.py

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+import os
+import torch
+import re
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
+from peft import PeftModel
+from threading import Thread
+
+# ==========================================
+# 1. SETUP & AUTHENTICATION
+# ==========================================
+# Mistral requires an HF token to download the base model. 
+# You must add your token in the HF Spaces "Settings" -> "Variables and secrets" as 'HF_TOKEN'
+hf_token = os.environ.get("HF_TOKEN")
+
+BASE_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
+ADAPTER_REPO = "your-username/Medical-Mistral-7B-LoRA" # <--- CHANGE THIS TO YOUR REPO
+
+print("Booting up Clinical AI Server...")
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Load Base Model in 4-bit precision
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True, 
+    bnb_4bit_quant_type="nf4", 
+    bnb_4bit_compute_dtype=torch.float16
+)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL, 
+    quantization_config=bnb_config, 
+    device_map="auto",
+    token=hf_token
+)
+
+# Merge Base Model with your Trained LoRA Adapter
+print("Downloading and attaching LoRA Adapter from Hub...")
+model = PeftModel.from_pretrained(base_model, ADAPTER_REPO, token=hf_token)
+model.eval()
+print("✅ Model Ready!")
+
+# ==========================================
+# 2. FORMATTING FUNCTION
+# ==========================================
+def format_and_clean(text):
+    end_triggers = ["regards", "hope this", "hope i", "let me know", "dr.", "thanks for"]
+    lower_text = text.lower()
+    cutoff = len(text)
+    
+    for trigger in end_triggers:
+        idx = lower_text.find(trigger)
+        if idx != -1 and idx < cutoff:
+            cutoff = idx
+            
+    clean = text[:cutoff].strip().replace('*', '')
+    
+    sentences = clean.split(". ")
+    capitalized = [s.capitalize() for s in sentences if s]
+    clean = ". ".join(capitalized)
+
+    clean = re.sub(r'(?i)assessment\s*:', '**Assessment:** ', clean)
+    clean = re.sub(r'(?i)analysis\s*:', '\n\n**Analysis:** ', clean)
+    clean = re.sub(r'(?i)recommended action\s*:', '\n\n**Recommended Action:** ', clean)
+    
+    return clean
+
+# ==========================================
+# 3. CHAT ENGINE & UI
+# ==========================================
+def clinical_chat(message, history):
+    prompt = f"""<s>[INST] You are a highly intelligent clinical AI. The user says: "{message}"
+    
+    If the user provides symptoms, diagnose them. If the user asks about a known condition, provide treatment advice.
+    
+    You MUST format your response exactly like this:
+    **Assessment:** [Name of the condition]
+    **Analysis:** [Brief explanation]
+    **Recommended Action:** [Medical advice]
+    
+    Do not include greetings, sign-offs, or links. [/INST]"""
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10.0)
+    
+    generation_kwargs = dict(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=300,
+        temperature=0.2, 
+        repetition_penalty=1.15, 
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    
+    partial_text = ""
+    for new_token in streamer:
+        partial_text += new_token
+        yield format_and_clean(partial_text)
+
+# Launch UI inside Docker container
+theme = gr.themes.Soft(primary_hue="blue", neutral_hue="slate")
+
+demo = gr.ChatInterface(
+    fn=clinical_chat,
+    title="⚕️ Clinical AI Diagnostic Assistant",
+    description="**Enter your symptoms or medical queries below for a professional analysis.**",
+    theme=theme,
+    examples=[
+        "I am having severe headache, body pain and strain in my neck.",
+        "What medicine should I take if I have high cholesterol?",
+        "I have been sneezing and coughing for 3 days."
+    ]
+)
+
+if __name__ == "__main__":
+    # 0.0.0.0 and port 7860 are strictly required for Docker on Hugging Face Spaces
+    demo.launch(server_name="0.0.0.0", server_port=7860)