Update app.py

app.py

@@ -3,8 +3,14 @@ from PIL import Image
 import pytesseract
 import pandas as pd
 import re
+import openai
 
 
+# Setup OpenAI API key (replace with your OpenAI API key)
+openai.api_key = "your-api-key-here"
+
+
+# Function to extract text from the image
 def extract_text(image):
     """
     Extract text from the image using Tesseract.
@@ -12,18 +18,16 @@ def extract_text(image):
     return pytesseract.image_to_string(image)
 
 
+# Function to parse and clean the extracted text
 def clean_and_parse_extracted_text(raw_text):
     """
     Parse and clean the raw text to extract structured data.
     """
-    # Split the text into lines and clean up
     lines = raw_text.split("\n")
     lines = [line.strip() for line in lines if line.strip()]
 
-    # Identify and extract rows with valid components
     data = []
     for line in lines:
-        # Match rows containing numeric ranges and values
         match = re.match(
             r"^(.*?)(\d+(\.\d+)?)(\s*-?\s*\d+(\.\d+)?\s*-?\s*\d+(\.\d+)?)?\s*([a-zA-Z/%]+)?\s*(H|L|Normal)?$",
             line,
@@ -42,21 +46,16 @@ def clean_and_parse_extracted_text(raw_text):
             unit = match.group(7)
             flag = "Normal"  # Default flag
 
-            # Determine the flag based on value and range
             if min_val is not None and max_val is not None:
                 if value < min_val:
                     flag = "L"
                 elif value > max_val:
                     flag = "H"
 
-            # Only append the data if the flag is abnormal (L or H)
             if flag != "Normal":
                 data.append([component, value, min_val, max_val, unit, flag])
 
-    # Create a DataFrame
     df = pd.DataFrame(data, columns=["Component", "Your Value", "Min", "Max", "Units", "Flag"])
-
-    # Fix misspellings and inconsistencies (if any known issues exist)
     correction_map = {
         "emoglobin": "Hemoglobin",
         "ematocrit": "Hematocrit",
@@ -69,16 +68,43 @@ def clean_and_parse_extracted_text(raw_text):
     return df
 
 
+# Function to generate AI-powered recommendations using OpenAI GPT
+def generate_medical_recommendation(test_results):
+    """
+    Generate medical recommendations using OpenAI GPT model based on abnormal test results.
+    """
+    # Create a structured input for the model
+    prompt = f"Given the following blood test results: {test_results}, provide medical recommendations for a patient."
+
+    response = openai.Completion.create(
+        model="gpt-4",  # Use GPT-4 for medical-based responses
+        prompt=prompt,
+        max_tokens=150
+    )
+
+    return response.choices[0].text.strip()
+
+
+# Function to display results
 def display_results(df):
     """
-    Display the flagged abnormalities in a table format.
+    Display the flagged abnormalities and medical recommendations in a table format.
     """
+    # Generate a summary of the abnormal test results for the AI model
+    abnormal_results = df[df['Flag'] != 'Normal'].to_string(index=False)
+    recommendation = generate_medical_recommendation(abnormal_results)
+    
+    # Display the DataFrame and the recommendation
+    st.subheader("Flagged Abnormalities")
     st.dataframe(df, use_container_width=True)
+    
+    st.subheader("Medical Recommendations from AI")
+    st.write(recommendation)
 
 
 # Streamlit app
-st.title("Blood Report Analyzer")
-st.write("Upload an image of a blood test report to analyze.")
+st.title("Blood Report Analyzer with AI Recommendations")
+st.write("Upload an image of a blood test report to analyze and get AI-powered recommendations.")
 
 uploaded_file = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"])
 
@@ -96,8 +122,7 @@ if uploaded_file is not None:
         # Parse the extracted text into a structured format
         parsed_data = clean_and_parse_extracted_text(extracted_text)
 
-        # Display the structured data (only abnormalities)
-        st.subheader("Flagged Abnormalities")
+        # Display the structured data with AI recommendations
         display_results(parsed_data)
 
     except Exception as e: