Create v2.txt

v2.txt

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import os
+import base64
+from together import Together
+
+def extract_medicines(api_key, image):
+    """
+    Extract medicine names from a prescription image using Together AI's Llama-Vision-Free model
+    """
+    # Check if API key is provided
+    if not api_key:
+        return "Please enter your Together API key."
+    
+    if image is None:
+        return "Please upload an image."
+    
+    try:
+        # Initialize Together client with the provided API key
+        client = Together(api_key=api_key)
+        
+        # Convert image to base64
+        with open(image, "rb") as img_file:
+            img_data = img_file.read()
+            b64_img = base64.b64encode(img_data).decode('utf-8')
+        
+        # Make API call with base64 encoded image
+        response = client.chat.completions.create(
+            model="meta-llama/Llama-Vision-Free",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an expert in identifying medicine names from prescription images."
+                },
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "Please extract the names of the medicines only."
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{b64_img}"
+                            }
+                        }
+                    ]
+                }
+            ]
+        )
+        
+        # Extract medicine names from response
+        medicine_list = response.choices[0].message.content
+        return medicine_list
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def recommend_medicine(api_key, medicine_name, csv_file=None):
+    """
+    Use Together API to recommend alternative medicines based on input medicine name
+    using data from the provided CSV file with specific column structure.
+    It will use AI to find similar medicines even if the exact name isn't in the dataset.
+    """
+    try:
+        # If CSV file is provided, use it; otherwise use default
+        if csv_file is not None:
+            # Read the uploaded CSV
+            if isinstance(csv_file, str):  # Path to default CSV
+                df = pd.read_csv(csv_file)
+            else:  # Uploaded file
+                df = pd.read_csv(csv_file.name)
+        else:
+            # Use the default medicine_dataset.csv in the current directory
+            try:
+                df = pd.read_csv("medicine_dataset.csv")
+            except FileNotFoundError:
+                return "Error: Default medicine_dataset.csv not found. Please upload a CSV file."
+        
+        # Check if medicine is in the dataset
+        medicine_exists = medicine_name in df['name'].values
+        
+        # Create a helpful context about the dataset to send to the LLM
+        dataset_overview = f"The dataset contains {len(df)} medicines with columns for name, substitutes, side effects, uses, chemical class, etc."
+        
+        # Sample of medicine names to give the model context
+        sample_names = df['name'].sample(min(20, len(df))).tolist()
+        medicine_sample = f"Sample medicines in the dataset: {', '.join(sample_names)}"
+        
+        # Extract specific medicine data if available
+        medicine_data = None
+        medicine_info_str = ""
+        if medicine_exists:
+            medicine_data = df[df['name'] == medicine_name]
+            medicine_info_str = medicine_data.to_string(index=False)
+        
+        # Create system prompt with dataset context
+        system_prompt = f"""You are a pharmaceutical expert system that recommends alternative medicines based on a comprehensive medicine dataset. The user has provided the medicine name "{medicine_name}".
+DATASET INFORMATION:
+{dataset_overview}
+{medicine_sample}
+The dataset has the following columns:
+- name: Medicine name
+- substitute0 through substitute4: Potential substitute medicines
+- sideEffect0 through sideEffect41: Possible side effects
+- use0 through use4: Medical uses
+- Chemical Class: The chemical classification
+- Habit Forming: Whether the medicine is habit-forming
+- Therapeutic Class: The therapeutic classification
+- Action Class: How the medicine works
+YOUR TASK:
+{"The medicine was found in the dataset with the following information:" if medicine_exists else "The medicine was NOT found in the dataset with an exact match. Your task is to:"}
+{medicine_info_str if medicine_exists else "1. Identify what kind of medicine this likely is based on its name (e.g., antibiotics, pain relievers, etc.)"}
+{'' if medicine_exists else "2. Look for medicines in the sample list that might be similar or serve similar purposes"}
+Please recommend alternative medicines for "{medicine_name}" with the following details for each:
+1. Name of the alternative medicine
+2. Why it's a good alternative (similar chemical composition, therapeutic use, etc.)
+3. Potential side effects to be aware of
+4. Usage recommendations
+5. Similarity to the original medicine (high, medium, low)
+Include at least 3-5 alternatives if possible.
+IMPORTANT:
+- If the medicine name contains strength or formulation (like "500mg" or "Duo"), focus on finding the base medicine first
+- Explain why these alternatives might be suitable replacements
+- Include appropriate medical disclaimers
+- Format your response clearly with headings for each alternative medicine
+"""
+
+        # Initialize Together client with the API key
+        client = Together(api_key=api_key)
+        
+        # Make API call
+        response = client.chat.completions.create(
+            model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+            messages=[
+                {
+                    "role": "system",
+                    "content": system_prompt
+                },
+                {
+                    "role": "user",
+                    "content": f"Please recommend alternatives for {medicine_name} based on the available information."
+                }
+            ],
+            max_tokens=2000,
+            temperature=0.7  # Slightly higher temperature for creative recommendations
+        )
+        
+        # Get the raw response
+        recommendation_text = response.choices[0].message.content
+        
+        # Add disclaimer
+        final_response = recommendation_text + "\n\n---\n\n**DISCLAIMER:** This information is for educational purposes only. Always consult with a healthcare professional before making any changes to your medication."
+        
+        return final_response
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def send_medicine_to_recommender(api_key, medicine_names, csv_file):
+    """
+    Takes medicine names extracted from prescription and gets recommendations
+    """
+    if not medicine_names or medicine_names.startswith("Error") or medicine_names.startswith("Please"):
+        return "Please extract valid medicine names first"
+    
+    # Extract the first medicine name from the list (assuming it's the first line or first item)
+    medicine_lines = medicine_names.strip().split('\n')
+    if not medicine_lines:
+        return "No valid medicine name found in extraction results"
+    
+    # Get the first medicine name (remove any bullet points or numbers)
+    first_medicine = medicine_lines[0]
+    # Clean up the medicine name (remove bullets, numbers, etc.)
+    first_medicine = first_medicine.lstrip('•-*0123456789. ').strip()
+    
+    # Check if we have a valid medicine name
+    if not first_medicine:
+        return "Could not identify a valid medicine name from extraction"
+    
+    # Call the recommend medicine function with the first extracted medicine
+    return recommend_medicine(api_key, first_medicine, csv_file)
+
+def analyze_full_prescription(api_key, medicine_names, csv_file):
+    """
+    Takes all extracted medicine names and analyzes their interactions and provides comprehensive information
+    """
+    if not medicine_names or medicine_names.startswith("Error") or medicine_names.startswith("Please"):
+        return "Please extract valid medicine names first"
+    
+    try:
+        # Parse the medicine names from the extracted text
+        medicine_lines = medicine_names.strip().split('\n')
+        cleaned_medicines = []
+        
+        # Clean up medicine names (remove bullets, numbers, etc.)
+        for medicine in medicine_lines:
+            cleaned_medicine = medicine.lstrip('•-*0123456789. ').strip()
+            if cleaned_medicine:
+                cleaned_medicines.append(cleaned_medicine)
+        
+        if not cleaned_medicines:
+            return "No valid medicine names found in extraction"
+        
+        # Create a prompt for the LLM to analyze the full prescription
+        medicines_list = ", ".join(cleaned_medicines)
+        
+        system_prompt = f"""You are a pharmaceutical expert analyzing a full prescription containing the following medicines: {medicines_list}.
+Please provide a comprehensive analysis including:
+1. Purpose: The likely medical condition(s) being treated with this combination of medicines
+2. Potential interactions: Any known drug interactions between these medicines
+3. Side effects: Common side effects to watch for when taking this combination
+4. Recommendations: General advice for the patient taking these medicines
+5. Questions for the doctor: Important questions the patient should ask their healthcare provider
+Base your analysis on pharmacological knowledge about these medicines and their typical uses.
+"""
+
+        # Initialize Together client with the API key
+        client = Together(api_key=api_key)
+        
+        # Make API call
+        response = client.chat.completions.create(
+            model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+            messages=[
+                {
+                    "role": "system",
+                    "content": system_prompt
+                },
+                {
+                    "role": "user",
+                    "content": f"Please analyze this prescription with the following medicines: {medicines_list}"
+                }
+            ],
+            max_tokens=2000,
+            temperature=0.3  # Lower temperature for more factual responses
+        )
+        
+        analysis_text = response.choices[0].message.content
+        
+        # Add disclaimer
+        final_response = analysis_text + "\n\n---\n\n**DISCLAIMER:** This analysis is for informational purposes only and should not replace professional medical advice. Always consult with your healthcare provider about your prescription."
+        
+        return final_response
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface with tabs for all functionalities
+with gr.Blocks(title="Medicine Assistant") as app:
+    gr.Markdown("# Medicine Assistant")
+    gr.Markdown("This application helps you extract medicine names from prescriptions, find alternative medicines, and analyze full prescriptions.")
+    
+    # API key input (shared between tabs)
+    api_key_input = gr.Textbox(
+        label="Together API Key", 
+        placeholder="Enter your Together API key here...",
+        type="password"
+    )
+    
+    # Create a file input for CSV that can be shared between tabs
+    csv_file_input = gr.File(
+        label="Upload Medicine CSV (Optional)", 
+        file_types=[".csv"],
+        type="filepath"  
+    )
+    gr.Markdown("If no CSV is uploaded, the app will use the default 'medicine_dataset.csv' file.")
+    
+    with gr.Tabs():
+        with gr.Tab("Prescription Medicine Extractor"):
+            gr.Markdown("## Prescription Medicine Extractor")
+            gr.Markdown("Upload a prescription image to extract medicine names using Together AI's Llama-Vision-Free model.")
+            
+            with gr.Row():
+                with gr.Column():
+                    image_input = gr.Image(type="filepath", label="Upload Prescription Image")
+                    extract_btn = gr.Button("Extract Medicines")
+                    
+                with gr.Column():
+                    extracted_output = gr.Textbox(label="Extracted Medicines", lines=10)
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    recommend_from_extract_btn = gr.Button("Get Recommendations for First Medicine", variant="primary")
+                    analyze_full_btn = gr.Button("Analyze Full Prescription", variant="secondary")
+                
+                with gr.Column(scale=2):
+                    output_tabs = gr.Tabs()
+                    with output_tabs:
+                        with gr.Tab("Recommendations"):
+                            recommendation_from_extract_output = gr.Markdown()
+                        with gr.Tab("Full Analysis"):
+                            full_analysis_output = gr.Markdown()
+            
+            # Connect the buttons to functions
+            extract_btn.click(
+                fn=extract_medicines, 
+                inputs=[api_key_input, image_input], 
+                outputs=extracted_output
+            )
+            
+            recommend_from_extract_btn.click(
+                fn=send_medicine_to_recommender,
+                inputs=[api_key_input, extracted_output, csv_file_input],
+                outputs=recommendation_from_extract_output
+            )
+            
+            analyze_full_btn.click(
+                fn=analyze_full_prescription,
+                inputs=[api_key_input, extracted_output, csv_file_input],
+                outputs=full_analysis_output
+            )
+            
+            gr.Markdown("""
+            ### How to use:
+            1. Enter your Together API key
+            2. Upload a clear image of a prescription
+            3. Click 'Extract Medicines' to see the identified medicines
+            4. Optionally upload a custom medicine dataset CSV
+            5. Choose to:
+               - Get alternatives for the first medicine
+               - Analyze the entire prescription for interactions and information
+            
+            ### Note:
+            - Your API key is used only for the current session
+            - For best results, ensure the prescription image is clear and readable
+            """)
+        
+        with gr.Tab("Medicine Alternative Recommender"):
+            gr.Markdown("## Medicine Alternative Recommender")
+            gr.Markdown("This tool recommends alternative medicines based on an input medicine name using the Together API.")
+            
+            with gr.Row():
+                with gr.Column():
+                    medicine_name = gr.Textbox(
+                        label="Medicine Name", 
+                        placeholder="Enter a medicine name (e.g., Augmentin 625 Duo)"
+                    )
+                    submit_btn = gr.Button("Get Recommendations", variant="primary")
+                
+                with gr.Column():
+                    recommendation_output = gr.Markdown()
+            
+            submit_btn.click(
+                recommend_medicine,
+                inputs=[api_key_input, medicine_name, csv_file_input],
+                outputs=recommendation_output
+            )
+
+            gr.Markdown("""
+            ## How to use this tool:
+            1. Enter your Together API key (same key used across the application)
+            2. Enter a medicine name - the AI will find it or match similar alternatives
+            3. Click "Get Recommendations" to see alternatives
+            
+            ### Features:
+            - Even if the exact medicine isn't in the database, the AI will try to find similar alternatives
+            - The system analyzes the medicine name to determine its likely purpose and composition
+            - Recommendations include substitutes, side effects, and usage information
+            """)
+    
+    gr.Markdown("""
+    ## About This Application
+    
+    This Medicine Assistant application combines powerful tools powered by Large Language Models:
+    
+    1. **Prescription Medicine Extractor**: Uses computer vision AI to identify medicine names from prescription images
+    2. **Medicine Alternative Recommender**: Provides detailed information about alternative medications
+    3. **Prescription Analyzer**: Analyzes entire prescriptions for potential interactions and insights
+    
+    All tools utilize the Together AI platform for advanced AI capabilities. Your API key is not stored and is only used to make API calls during your active session.
+    
+    ### Important Note
+    
+    This application is for informational purposes only. Always consult with a healthcare professional before making any changes to your medication regimen.
+    """)
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()