Update app.py

app.py

@@ -6,157 +6,7 @@ import torch
 from PIL import Image
 from transformers import MllamaForConditionalGeneration, AutoProcessor
 
-SCHEMA_DEFINITION= """{
-  "$schema": "http://json-schema.org/draft-04/schema#",
-  "type": "object",
-  "properties": {
-    "Issue_Description": {
-      "type": "string"
-    },
-    "Root_Cause_Analysis": {
-      "type": "object",
-      "properties": {
-        "LED_Analysis": {
-          "type": "object",
-          "properties": {
-            "Color": {
-              "type": "string"
-            },
-            "Pattern": {
-              "type": "string"
-            },
-            "Indicates": {
-              "type": "string"
-            }
-          },
-          "required": [
-            "Color",
-            "Pattern",
-            "Indicates"
-          ]
-        },
-        "Error_Code": {
-          "type": "string"
-        },
-        "Possible_Cause": {
-          "type": "string"
-        }
-      },
-      "required": [
-        "LED_Analysis",
-        "Error_Code",
-        "Possible_Cause"
-      ]
-    },
-    "Step_by_Step_Troubleshooting": {
-      "type": "array",
-      "items": [
-        {
-          "type": "object",
-          "properties": {
-            "Action": {
-              "type": "string"
-            },
-            "Details": {
-              "type": "string"
-            },
-            "Expected Outcome": {
-              "type": "string"
-            }
-          },
-          "required": [
-            "Action",
-            "Details",
-            "Expected Outcome"
-          ]
-        },
-        {
-          "type": "object",
-          "properties": {
-            "Action": {
-              "type": "string"
-            },
-            "Details": {
-              "type": "string"
-            },
-            "Expected Outcome": {
-              "type": "string"
-            }
-          },
-          "required": [
-            "Action",
-            "Details",
-            "Expected Outcome"
-          ]
-        },
-        {
-          "type": "object",
-          "properties": {
-            "Action": {
-              "type": "string"
-            },
-            "Details": {
-              "type": "string"
-            },
-            "Expected Outcome": {
-              "type": "string"
-            }
-          },
-          "required": [
-            "Action",
-            "Details",
-            "Expected Outcome"
-          ]
-        },
-        {
-          "type": "object",
-          "properties": {
-            "Action": {
-              "type": "string"
-            },
-            "Details": {
-              "type": "string"
-            },
-            "Expected Outcome": {
-              "type": "string"
-            }
-          },
-          "required": [
-            "Action",
-            "Details",
-            "Expected Outcome"
-          ]
-        }
-      ]
-    },
-    "Recommended_Actions": {
-      "type": "object",
-      "properties": {
-        "Immediate_Action": {
-          "type": "string"
-        },
-        "If_Unresolved": {
-          "type": "string"
-        },
-        "Preventative_Measure": {
-          "type": "string"
-        }
-      },
-      "required": [
-        "Immediate_Action",
-        "If_Unresolved",
-        "Preventative_Measure"
-      ]
-    }
-  },
-  "required": [
-    "Issue_Description",
-    "Root_Cause_Analysis",
-    "Step_by_Step_Troubleshooting",
-    "Recommended_Actions"
-  ]
-}"""
-SYSTEM_INSTRUCTION="You are a router troubleshooter. Your job is to analyze the provided router image, identify potential issues such as faulty connections, incorrect LED patterns, or error codes, and offer precise troubleshooting steps. Based on your analysis, generate a detailed observation that includes a root cause analysis, step-by-step actions for resolving the issue, and recommended preventive measures. The output must be in JSON format as per the following schema, ensuring users can easily follow and implement the suggested solutions.\n" + SCHEMA_DEFINITION
+SYSTEM_INSTRUCTION="You are a medical report interpreter. Your task is to analyze the provided medical reports, identify key medical terms, diagnoses, or abnormalities, and provide a clear interpretation. Based on your analysis, generate a detailed summary that includes an explanation of the findings, recommended actions, and any additional insights for the patient or healthcare provider. Ensure your output is structured and easily understandable for both professionals and non-professionals."
 
 
 model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
@@ -181,27 +31,7 @@ def extract_assistant_reply(input_string):
     assistant_reply = input_string[start_index:].strip()
     return assistant_reply
 
-def extract_json_from_markdown(markdown_text):
-    """Extract JSON or code block from markdown text."""
-    try:
-        # Find the start and end of the code block (with or without "json")
-        start_idx = markdown_text.find('```')
-        end_idx = markdown_text.find('```', start_idx + 3)
-        
-        # If the block starts with '```json', skip the 'json' part
-        if markdown_text[start_idx:start_idx + 7] == '```json':
-            start_idx += len('```json')
-        else:
-            start_idx += len('```')
 
-        # Extract and clean up the code block (json or not)
-        json_str = markdown_text[start_idx:end_idx].strip()
-
-        # Try to load it as JSON
-        return json.loads(json_str)
-    except Exception as e:
-        print(f"Error extracting JSON: {e}")
-        return None
 
 @spaces.GPU
 def diagnose_router(image):
@@ -219,51 +49,6 @@ def diagnose_router(image):
     print(output)
     markdown_text = processor.decode(output[0])
     print(markdown_text)
-    # Extract JSON from the markdown text
-    #result = extract_json_from_markdown(markdown_text)
-
-    
-    #print (result)
-    
-    # Generate HTML content for structured display
-    # html_output = f"""
-    # <div style="font-family: Arial, sans-serif; color: #333;">
-    #     <h2>Router Diagnosis</h2>
-    #     <h3>Issue Description</h3>
-    #     <p><strong>{result['Issue_Description']}</strong></p>
-        
-    #     <h3>Root Cause Analysis</h3>
-    #     <ul>
-    #         <li><strong>LED Color:</strong> {result['Root_Cause_Analysis']['LED_Analysis']['Color']}</li>
-    #         <li><strong>LED Pattern:</strong> {result['Root_Cause_Analysis']['LED_Analysis']['Pattern']}</li>
-    #         <li><strong>Indicates:</strong> {result['Root_Cause_Analysis']['LED_Analysis']['Indicates']}</li>
-    #         <li><strong>Error Code:</strong> {result['Root_Cause_Analysis']['Error_Code']}</li>
-    #         <li><strong>Possible Cause:</strong> {result['Root_Cause_Analysis']['Possible_Cause']}</li>
-    #     </ul>
-        
-    #     <h3>Step-by-Step Troubleshooting</h3>
-    #     <ol>
-    # """
-
-    # # Loop through each step in the troubleshooting process (now a list)
-    # for step in result["Step_by_Step_Troubleshooting"]:
-    #     html_output += f"""
-    #         <li><strong>{step['Action']}</strong>: {step['Details']}<br/>
-    #         <em>Expected Outcome:</em> {step['Expected Outcome']}</li>
-    #     """
-    
-    # # Adding the Recommended Actions section
-    # html_output += f"""
-    #     </ol>
-
-    #     <h3>Recommended Actions</h3>
-    #     <ul>
-    #         <li><strong>Immediate Action:</strong> {result['Recommended_Actions']['Immediate_Action']}</li>
-    #         <li><strong>If Unresolved:</strong> {result['Recommended_Actions']['If_Unresolved']}</li>
-    #         <li><strong>Preventative Measure:</strong> {result['Recommended_Actions']['Preventative_Measure']}</li>
-    #     </ul>
-    # </div>
-    # """
     
     markdown_text=extract_assistant_reply(markdown_text)
     html_output = markdown.markdown(markdown_text)