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+FROM python:3.9-slim
+
+WORKDIR /code
+
+# Install git
+RUN apt-get update && apt-get install -y git
+
+# Install PyTorch and other dependencies
+COPY requirements.txt /code/
+RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cu118
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the application code
+COPY app.py /code/
+
+# Set environment variables
+ENV PYTHONPATH=/code
+ENV GRADIO_SERVER_NAME=0.0.0.0
+ENV GRADIO_SERVER_PORT=7860
+
+# Expose the port
+EXPOSE 7860
+
+# Run the application
+CMD ["python", "app.py"]

app.py

@@ -1,64 +1,206 @@
+import os
+from typing import Optional, List, Dict
+from contextlib import asynccontextmanager
+import re
+import json
+
+from fastapi import FastAPI, HTTPException, status
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
+class MedicalReport(BaseModel):
+    text: str
+
+class ReportResponse(BaseModel):
+    assessment: str
+
+class MedicalAssessmentModel:
+    def __init__(self):
+        # Initialize model and tokenizer
+        model_name = "meta-llama/Llama-2-7b-chat-hf"  # or any other model you prefer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+
+    def generate_response(self, messages: List[Dict]) -> str:
+        # Combine messages into a single prompt
+        prompt = ""
+        for msg in messages:
+            role = msg['role']
+            content = msg['content']
+            prompt += f"{role}: {content}\n"
+
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=512,
+                temperature=0.7,
+                do_sample=True,
+                top_p=0.9,
+                num_return_sequences=1,
+            )
+        
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        # Extract only the generated part
+        response = response[len(self.tokenizer.decode(inputs['input_ids'][0], skip_special_tokens=True)):]
+        return response.strip()
+
+    def run_env1(self, patient_text: str) -> str:
+        """Tool Selection Stage"""
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a medical professional expert in selecting appropriate clinical risk assessment tools."
+            },
+            {
+                "role": "user",
+                "content": f"""Based on the patient's discharge summary, identify potential disease risks and assessment needs.
+
+Patient Information:
+{patient_text}
+
+Please analyze:
+1. Primary health concerns
+2. Risk factors identified
+3. Potential complications
+4. Areas requiring risk assessment"""
+            }
+        ]
+        return self.generate_response(messages)
+
+    def run_env2(self, patient_text: str, env1_output: str) -> str:
+        """Parameter Extraction Stage"""
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a medical professional expert in extracting clinical parameters from patient records."
+            },
+            {
+                "role": "user",
+                "content": f"""Extract relevant clinical parameters from the patient's information.
+
+Patient Information:
+{patient_text}
+
+Previous Analysis:
+{env1_output}
+
+Please provide:
+1. Key vital signs
+2. Relevant lab values
+3. Clinical findings
+4. Risk factors identified"""
+            }
+        ]
+        return self.generate_response(messages)
+
+    def run_env3(self, patient_text: str, env1_output: str, env2_output: str) -> str:
+        """Risk Interpretation Stage"""
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a medical expert specialized in clinical risk assessment and interpretation."
+            },
+            {
+                "role": "user",
+                "content": f"""Interpret the identified risks and clinical parameters.
+
+Patient Information:
+{patient_text}
+
+Risk Analysis:
+{env1_output}
+
+Clinical Parameters:
+{env2_output}
+
+Please provide:
+1. Risk level assessment for each identified condition
+2. Clinical significance of findings
+3. Interaction between different risk factors
+4. Severity assessment"""
+            }
+        ]
+        return self.generate_response(messages)
+
+    def run_env4(self, patient_text: str, env1_output: str, env2_output: str, env3_output: str) -> str:
+        """Final Assessment Stage"""
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a medical expert specialized in comprehensive risk assessment and patient care planning."
+            },
+            {
+                "role": "user",
+                "content": f"""Based on all previous analyses, provide a comprehensive assessment of the patient's disease risks.
+
+Patient Information:
+{patient_text}
+
+Previous Analyses:
+Risk Identification: {env1_output}
+Parameter Analysis: {env2_output}
+Risk Interpretation: {env3_output}
+
+Please provide:
+1. Summary of significant disease risks identified
+2. Overall risk assessment
+3. Key areas of concern
+4. Recommended monitoring or preventive measures
+5. Suggestions for risk mitigation
+
+Format the response in clear sections with headers."""
+            }
+        ]
+        return self.generate_response(messages)
+
+    def process_report(self, patient_text: str) -> str:
+        """Process the entire pipeline and return ENV4 output"""
+        try:
+            # Run all environments sequentially
+            env1_output = self.run_env1(patient_text)
+            env2_output = self.run_env2(patient_text, env1_output)
+            env3_output = self.run_env3(patient_text, env1_output, env2_output)
+            env4_output = self.run_env4(patient_text, env1_output, env2_output, env3_output)
+            
+            return env4_output
+        except Exception as e:
+            return f"Error in processing: {str(e)}"
+
+def create_gradio_interface():
+    model = MedicalAssessmentModel()
+    
+    def analyze_text(text):
+        return model.process_report(text)
+    
+    iface = gr.Interface(
+        fn=analyze_text,
+        inputs=gr.Textbox(
+            lines=10, 
+            placeholder="Enter patient medical report here...",
+            label="Medical Report"
+        ),
+        outputs=gr.Textbox(
+            lines=15,
+            label="Risk Assessment Report"
+        ),
+        title="Medical Report Risk Assessment",
+        description="Enter a medical report to get a comprehensive risk assessment. The system will analyze the report through multiple stages and provide a final assessment.",
+        examples=[
+            ["Patient was admitted with chest pain and shortness of breath. History of hypertension and diabetes. BP 160/95, HR 98. Recent smoker with 30 pack-year history."],
+            ["83-year-old female presents with confusion and fever. Recent fall at home. History of osteoporosis and mild cognitive impairment. Lives alone. Temperature 38.5C, BP 135/85."]
+        ]
+    )
+    return iface
 
 if __name__ == "__main__":
-    demo.launch()
+    iface = create_gradio_interface()
+    iface.launch(server_name="0.0.0.0", server_port=7860)

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+transformers
+torch
+gradio>=4.0.0
+pydantic