Create app.py

app.py

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+# app.py
+import os
+import gradio as gr
+import torch
+import requests
+from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
+from monai.networks.nets import DenseNet121
+import torchxrayvision as xrv
+
+# Configuration
+DEEPSEEK_API_URL = "https://api.deepseek.com/v1/chat/completions"
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")  # Set in Hugging Face secrets
+
+DISCLAIMER = """
+<div style="color: red; border: 2px solid red; padding: 15px; margin: 10px;">
+⚠️ WARNING: This is a prototype demonstration only. NOT ACTUAL MEDICAL ADVICE. 
+DO NOT USE FOR REAL HEALTH DECISIONS. CONSULT LICENSED PROFESSIONALS.
+</div>
+"""
+
+class MedicalAssistant:
+    def __init__(self):
+        # Medical imaging models
+        self.medical_models = self._init_imaging_models()
+        
+        # Clinical text processing
+        self.prescription_parser = pipeline(
+            "token-classification", 
+            model="obi/deid_bert_i2b2",
+            tokenizer="obi/deid_bert_i2b2"
+        )
+        
+        # Safety systems
+        self.safety_filter = pipeline(
+            "text-classification", 
+            model="Hate-speech-CNERG/dehatebert-mono-english"
+        )
+
+    def _init_imaging_models(self):
+        """Initialize medical imaging models"""
+        return {
+            "xray": xrv.models.DenseNet(weights="densenet121-res224-all"),
+            "ct": DenseNet121(spatial_dims=3, in_channels=1, out_channels=14),
+            "histo": torch.hub.load('pytorch/vision', 'resnet50', pretrained=True)
+        }
+
+    def query_deepseek(self, prompt: str):
+        """Query DeepSeek medical research API"""
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {DEEPSEEK_API_KEY}"
+        }
+        
+        payload = {
+            "model": "deepseek-medical-1.0",
+            "messages": [{
+                "role": "user",
+                "content": f"MEDICAL PROMPT: {prompt}\nRespond with latest research-supported information. Cite sources."
+            }],
+            "temperature": 0.2,
+            "max_tokens": 500
+        }
+
+        try:
+            response = requests.post(DEEPSEEK_API_URL, json=payload, headers=headers)
+            response.raise_for_status()
+            return response.json()['choices'][0]['message']['content']
+        except Exception as e:
+            return f"API Error: {str(e)}"
+
+    def analyze_image(self, image_path: str, modality: str):
+        """Medical image analysis"""
+        try:
+            img = self._preprocess_image(image_path, modality)
+            
+            if modality == "xray":
+                output = self.medical_models["xray"](img)
+                return xrv.datasets.default_pathologies[torch.argmax(output)]
+            elif modality == "ct":
+                output = self.medical_models["ct"](img)
+                return "CT analysis placeholder"
+            elif modality == "histo":
+                output = self.medical_models["histo"](img)
+                return "Histopathology analysis placeholder"
+        except Exception as e:
+            return f"Image analysis error: {str(e)}"
+
+    def parse_prescription(self, text: str):
+        """Clinical text parsing"""
+        entities = self.prescription_parser(text)
+        return {
+            "medications": [ent for ent in entities if ent['entity'] == 'MEDICATION'],
+            "dosage": [ent for ent in entities if ent['entity'] == 'DOSAGE']
+        }
+
+    def generate_response(self, query: str, context: dict):
+        """Generate safe, research-backed response"""
+        # Construct enhanced prompt
+        research_prompt = f"""
+        Medical Query: {query}
+        Context:
+        - Image Findings: {context.get('image_analysis', 'N/A')}
+        - Prescription Data: {context.get('prescription', 'N/A')}
+        
+        Requirements:
+        1. Provide evidence-based medical information
+        2. Cite recent research (post-2020 when possible)
+        3. Include safety considerations
+        4. Note confidence level
+        """
+        
+        # Get DeepSeek research response
+        raw_response = self.query_deepseek(research_prompt)
+        
+        # Apply safety filters
+        if self._is_unsafe(raw_response):
+            return "I cannot provide advice on this matter. Please consult a healthcare professional."
+            
+        return self._add_disclaimer(raw_response)
+
+    def _is_unsafe(self, text: str):
+        """Content safety check"""
+        return self.safety_filter(text)[0]['label'] == 'HATE'
+
+    def _add_disclaimer(self, text: str):
+        """Add legal disclaimer to response"""
+        return f"{text}\n\n---\n⚠️ This information is for research purposes only. Not medical advice."
+
+    def _preprocess_image(self, image_path: str, modality: str):
+        """Image preprocessing placeholder"""
+        return torch.rand((1, 224, 224))
+
+# Initialize system
+assistant = MedicalAssistant()
+
+def process_input(query, image, prescription):
+    context = {}
+    
+    if image is not None:
+        context["image_analysis"] = assistant.analyze_image(image, "xray")
+    
+    if prescription:
+        context["prescription"] = assistant.parse_prescription(prescription)
+    
+    return assistant.generate_response(query, context)
+
+# Gradio interface
+interface = gr.Interface(
+    fn=process_input,
+    inputs=[
+        gr.Textbox(label="Medical Query", placeholder="Enter your medical question..."),
+        gr.Image(label="Medical Imaging", type="filepath"),
+        gr.Textbox(label="Prescription Text")
+    ],
+    outputs=gr.Textbox(label="Research-Backed Response"),
+    title="AI Medical Research Assistant",
+    description=DISCLAIMER,
+    allow_flagging="never"
+)
+
+interface.launch() make a requirements and readme file for this code for huggins face