Create v2.txt
Browse files
v2.txt
ADDED
@@ -0,0 +1,381 @@
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1 |
+
import gradio as gr
|
2 |
+
import pandas as pd
|
3 |
+
import numpy as np
|
4 |
+
import os
|
5 |
+
import base64
|
6 |
+
from together import Together
|
7 |
+
|
8 |
+
def extract_medicines(api_key, image):
|
9 |
+
"""
|
10 |
+
Extract medicine names from a prescription image using Together AI's Llama-Vision-Free model
|
11 |
+
"""
|
12 |
+
# Check if API key is provided
|
13 |
+
if not api_key:
|
14 |
+
return "Please enter your Together API key."
|
15 |
+
|
16 |
+
if image is None:
|
17 |
+
return "Please upload an image."
|
18 |
+
|
19 |
+
try:
|
20 |
+
# Initialize Together client with the provided API key
|
21 |
+
client = Together(api_key=api_key)
|
22 |
+
|
23 |
+
# Convert image to base64
|
24 |
+
with open(image, "rb") as img_file:
|
25 |
+
img_data = img_file.read()
|
26 |
+
b64_img = base64.b64encode(img_data).decode('utf-8')
|
27 |
+
|
28 |
+
# Make API call with base64 encoded image
|
29 |
+
response = client.chat.completions.create(
|
30 |
+
model="meta-llama/Llama-Vision-Free",
|
31 |
+
messages=[
|
32 |
+
{
|
33 |
+
"role": "system",
|
34 |
+
"content": "You are an expert in identifying medicine names from prescription images."
|
35 |
+
},
|
36 |
+
{
|
37 |
+
"role": "user",
|
38 |
+
"content": [
|
39 |
+
{
|
40 |
+
"type": "text",
|
41 |
+
"text": "Please extract the names of the medicines only."
|
42 |
+
},
|
43 |
+
{
|
44 |
+
"type": "image_url",
|
45 |
+
"image_url": {
|
46 |
+
"url": f"data:image/jpeg;base64,{b64_img}"
|
47 |
+
}
|
48 |
+
}
|
49 |
+
]
|
50 |
+
}
|
51 |
+
]
|
52 |
+
)
|
53 |
+
|
54 |
+
# Extract medicine names from response
|
55 |
+
medicine_list = response.choices[0].message.content
|
56 |
+
return medicine_list
|
57 |
+
|
58 |
+
except Exception as e:
|
59 |
+
return f"Error: {str(e)}"
|
60 |
+
|
61 |
+
def recommend_medicine(api_key, medicine_name, csv_file=None):
|
62 |
+
"""
|
63 |
+
Use Together API to recommend alternative medicines based on input medicine name
|
64 |
+
using data from the provided CSV file with specific column structure.
|
65 |
+
It will use AI to find similar medicines even if the exact name isn't in the dataset.
|
66 |
+
"""
|
67 |
+
try:
|
68 |
+
# If CSV file is provided, use it; otherwise use default
|
69 |
+
if csv_file is not None:
|
70 |
+
# Read the uploaded CSV
|
71 |
+
if isinstance(csv_file, str): # Path to default CSV
|
72 |
+
df = pd.read_csv(csv_file)
|
73 |
+
else: # Uploaded file
|
74 |
+
df = pd.read_csv(csv_file.name)
|
75 |
+
else:
|
76 |
+
# Use the default medicine_dataset.csv in the current directory
|
77 |
+
try:
|
78 |
+
df = pd.read_csv("medicine_dataset.csv")
|
79 |
+
except FileNotFoundError:
|
80 |
+
return "Error: Default medicine_dataset.csv not found. Please upload a CSV file."
|
81 |
+
|
82 |
+
# Check if medicine is in the dataset
|
83 |
+
medicine_exists = medicine_name in df['name'].values
|
84 |
+
|
85 |
+
# Create a helpful context about the dataset to send to the LLM
|
86 |
+
dataset_overview = f"The dataset contains {len(df)} medicines with columns for name, substitutes, side effects, uses, chemical class, etc."
|
87 |
+
|
88 |
+
# Sample of medicine names to give the model context
|
89 |
+
sample_names = df['name'].sample(min(20, len(df))).tolist()
|
90 |
+
medicine_sample = f"Sample medicines in the dataset: {', '.join(sample_names)}"
|
91 |
+
|
92 |
+
# Extract specific medicine data if available
|
93 |
+
medicine_data = None
|
94 |
+
medicine_info_str = ""
|
95 |
+
if medicine_exists:
|
96 |
+
medicine_data = df[df['name'] == medicine_name]
|
97 |
+
medicine_info_str = medicine_data.to_string(index=False)
|
98 |
+
|
99 |
+
# Create system prompt with dataset context
|
100 |
+
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}".
|
101 |
+
DATASET INFORMATION:
|
102 |
+
{dataset_overview}
|
103 |
+
{medicine_sample}
|
104 |
+
The dataset has the following columns:
|
105 |
+
- name: Medicine name
|
106 |
+
- substitute0 through substitute4: Potential substitute medicines
|
107 |
+
- sideEffect0 through sideEffect41: Possible side effects
|
108 |
+
- use0 through use4: Medical uses
|
109 |
+
- Chemical Class: The chemical classification
|
110 |
+
- Habit Forming: Whether the medicine is habit-forming
|
111 |
+
- Therapeutic Class: The therapeutic classification
|
112 |
+
- Action Class: How the medicine works
|
113 |
+
YOUR TASK:
|
114 |
+
{"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:"}
|
115 |
+
{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.)"}
|
116 |
+
{'' if medicine_exists else "2. Look for medicines in the sample list that might be similar or serve similar purposes"}
|
117 |
+
Please recommend alternative medicines for "{medicine_name}" with the following details for each:
|
118 |
+
1. Name of the alternative medicine
|
119 |
+
2. Why it's a good alternative (similar chemical composition, therapeutic use, etc.)
|
120 |
+
3. Potential side effects to be aware of
|
121 |
+
4. Usage recommendations
|
122 |
+
5. Similarity to the original medicine (high, medium, low)
|
123 |
+
Include at least 3-5 alternatives if possible.
|
124 |
+
IMPORTANT:
|
125 |
+
- If the medicine name contains strength or formulation (like "500mg" or "Duo"), focus on finding the base medicine first
|
126 |
+
- Explain why these alternatives might be suitable replacements
|
127 |
+
- Include appropriate medical disclaimers
|
128 |
+
- Format your response clearly with headings for each alternative medicine
|
129 |
+
"""
|
130 |
+
|
131 |
+
# Initialize Together client with the API key
|
132 |
+
client = Together(api_key=api_key)
|
133 |
+
|
134 |
+
# Make API call
|
135 |
+
response = client.chat.completions.create(
|
136 |
+
model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
|
137 |
+
messages=[
|
138 |
+
{
|
139 |
+
"role": "system",
|
140 |
+
"content": system_prompt
|
141 |
+
},
|
142 |
+
{
|
143 |
+
"role": "user",
|
144 |
+
"content": f"Please recommend alternatives for {medicine_name} based on the available information."
|
145 |
+
}
|
146 |
+
],
|
147 |
+
max_tokens=2000,
|
148 |
+
temperature=0.7 # Slightly higher temperature for creative recommendations
|
149 |
+
)
|
150 |
+
|
151 |
+
# Get the raw response
|
152 |
+
recommendation_text = response.choices[0].message.content
|
153 |
+
|
154 |
+
# Add disclaimer
|
155 |
+
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."
|
156 |
+
|
157 |
+
return final_response
|
158 |
+
|
159 |
+
except Exception as e:
|
160 |
+
return f"Error: {str(e)}"
|
161 |
+
|
162 |
+
def send_medicine_to_recommender(api_key, medicine_names, csv_file):
|
163 |
+
"""
|
164 |
+
Takes medicine names extracted from prescription and gets recommendations
|
165 |
+
"""
|
166 |
+
if not medicine_names or medicine_names.startswith("Error") or medicine_names.startswith("Please"):
|
167 |
+
return "Please extract valid medicine names first"
|
168 |
+
|
169 |
+
# Extract the first medicine name from the list (assuming it's the first line or first item)
|
170 |
+
medicine_lines = medicine_names.strip().split('\n')
|
171 |
+
if not medicine_lines:
|
172 |
+
return "No valid medicine name found in extraction results"
|
173 |
+
|
174 |
+
# Get the first medicine name (remove any bullet points or numbers)
|
175 |
+
first_medicine = medicine_lines[0]
|
176 |
+
# Clean up the medicine name (remove bullets, numbers, etc.)
|
177 |
+
first_medicine = first_medicine.lstrip('•-*0123456789. ').strip()
|
178 |
+
|
179 |
+
# Check if we have a valid medicine name
|
180 |
+
if not first_medicine:
|
181 |
+
return "Could not identify a valid medicine name from extraction"
|
182 |
+
|
183 |
+
# Call the recommend medicine function with the first extracted medicine
|
184 |
+
return recommend_medicine(api_key, first_medicine, csv_file)
|
185 |
+
|
186 |
+
def analyze_full_prescription(api_key, medicine_names, csv_file):
|
187 |
+
"""
|
188 |
+
Takes all extracted medicine names and analyzes their interactions and provides comprehensive information
|
189 |
+
"""
|
190 |
+
if not medicine_names or medicine_names.startswith("Error") or medicine_names.startswith("Please"):
|
191 |
+
return "Please extract valid medicine names first"
|
192 |
+
|
193 |
+
try:
|
194 |
+
# Parse the medicine names from the extracted text
|
195 |
+
medicine_lines = medicine_names.strip().split('\n')
|
196 |
+
cleaned_medicines = []
|
197 |
+
|
198 |
+
# Clean up medicine names (remove bullets, numbers, etc.)
|
199 |
+
for medicine in medicine_lines:
|
200 |
+
cleaned_medicine = medicine.lstrip('•-*0123456789. ').strip()
|
201 |
+
if cleaned_medicine:
|
202 |
+
cleaned_medicines.append(cleaned_medicine)
|
203 |
+
|
204 |
+
if not cleaned_medicines:
|
205 |
+
return "No valid medicine names found in extraction"
|
206 |
+
|
207 |
+
# Create a prompt for the LLM to analyze the full prescription
|
208 |
+
medicines_list = ", ".join(cleaned_medicines)
|
209 |
+
|
210 |
+
system_prompt = f"""You are a pharmaceutical expert analyzing a full prescription containing the following medicines: {medicines_list}.
|
211 |
+
Please provide a comprehensive analysis including:
|
212 |
+
1. Purpose: The likely medical condition(s) being treated with this combination of medicines
|
213 |
+
2. Potential interactions: Any known drug interactions between these medicines
|
214 |
+
3. Side effects: Common side effects to watch for when taking this combination
|
215 |
+
4. Recommendations: General advice for the patient taking these medicines
|
216 |
+
5. Questions for the doctor: Important questions the patient should ask their healthcare provider
|
217 |
+
Base your analysis on pharmacological knowledge about these medicines and their typical uses.
|
218 |
+
"""
|
219 |
+
|
220 |
+
# Initialize Together client with the API key
|
221 |
+
client = Together(api_key=api_key)
|
222 |
+
|
223 |
+
# Make API call
|
224 |
+
response = client.chat.completions.create(
|
225 |
+
model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
|
226 |
+
messages=[
|
227 |
+
{
|
228 |
+
"role": "system",
|
229 |
+
"content": system_prompt
|
230 |
+
},
|
231 |
+
{
|
232 |
+
"role": "user",
|
233 |
+
"content": f"Please analyze this prescription with the following medicines: {medicines_list}"
|
234 |
+
}
|
235 |
+
],
|
236 |
+
max_tokens=2000,
|
237 |
+
temperature=0.3 # Lower temperature for more factual responses
|
238 |
+
)
|
239 |
+
|
240 |
+
analysis_text = response.choices[0].message.content
|
241 |
+
|
242 |
+
# Add disclaimer
|
243 |
+
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."
|
244 |
+
|
245 |
+
return final_response
|
246 |
+
|
247 |
+
except Exception as e:
|
248 |
+
return f"Error: {str(e)}"
|
249 |
+
|
250 |
+
# Create Gradio interface with tabs for all functionalities
|
251 |
+
with gr.Blocks(title="Medicine Assistant") as app:
|
252 |
+
gr.Markdown("# Medicine Assistant")
|
253 |
+
gr.Markdown("This application helps you extract medicine names from prescriptions, find alternative medicines, and analyze full prescriptions.")
|
254 |
+
|
255 |
+
# API key input (shared between tabs)
|
256 |
+
api_key_input = gr.Textbox(
|
257 |
+
label="Together API Key",
|
258 |
+
placeholder="Enter your Together API key here...",
|
259 |
+
type="password"
|
260 |
+
)
|
261 |
+
|
262 |
+
# Create a file input for CSV that can be shared between tabs
|
263 |
+
csv_file_input = gr.File(
|
264 |
+
label="Upload Medicine CSV (Optional)",
|
265 |
+
file_types=[".csv"],
|
266 |
+
type="filepath"
|
267 |
+
)
|
268 |
+
gr.Markdown("If no CSV is uploaded, the app will use the default 'medicine_dataset.csv' file.")
|
269 |
+
|
270 |
+
with gr.Tabs():
|
271 |
+
with gr.Tab("Prescription Medicine Extractor"):
|
272 |
+
gr.Markdown("## Prescription Medicine Extractor")
|
273 |
+
gr.Markdown("Upload a prescription image to extract medicine names using Together AI's Llama-Vision-Free model.")
|
274 |
+
|
275 |
+
with gr.Row():
|
276 |
+
with gr.Column():
|
277 |
+
image_input = gr.Image(type="filepath", label="Upload Prescription Image")
|
278 |
+
extract_btn = gr.Button("Extract Medicines")
|
279 |
+
|
280 |
+
with gr.Column():
|
281 |
+
extracted_output = gr.Textbox(label="Extracted Medicines", lines=10)
|
282 |
+
|
283 |
+
with gr.Row():
|
284 |
+
with gr.Column(scale=1):
|
285 |
+
recommend_from_extract_btn = gr.Button("Get Recommendations for First Medicine", variant="primary")
|
286 |
+
analyze_full_btn = gr.Button("Analyze Full Prescription", variant="secondary")
|
287 |
+
|
288 |
+
with gr.Column(scale=2):
|
289 |
+
output_tabs = gr.Tabs()
|
290 |
+
with output_tabs:
|
291 |
+
with gr.Tab("Recommendations"):
|
292 |
+
recommendation_from_extract_output = gr.Markdown()
|
293 |
+
with gr.Tab("Full Analysis"):
|
294 |
+
full_analysis_output = gr.Markdown()
|
295 |
+
|
296 |
+
# Connect the buttons to functions
|
297 |
+
extract_btn.click(
|
298 |
+
fn=extract_medicines,
|
299 |
+
inputs=[api_key_input, image_input],
|
300 |
+
outputs=extracted_output
|
301 |
+
)
|
302 |
+
|
303 |
+
recommend_from_extract_btn.click(
|
304 |
+
fn=send_medicine_to_recommender,
|
305 |
+
inputs=[api_key_input, extracted_output, csv_file_input],
|
306 |
+
outputs=recommendation_from_extract_output
|
307 |
+
)
|
308 |
+
|
309 |
+
analyze_full_btn.click(
|
310 |
+
fn=analyze_full_prescription,
|
311 |
+
inputs=[api_key_input, extracted_output, csv_file_input],
|
312 |
+
outputs=full_analysis_output
|
313 |
+
)
|
314 |
+
|
315 |
+
gr.Markdown("""
|
316 |
+
### How to use:
|
317 |
+
1. Enter your Together API key
|
318 |
+
2. Upload a clear image of a prescription
|
319 |
+
3. Click 'Extract Medicines' to see the identified medicines
|
320 |
+
4. Optionally upload a custom medicine dataset CSV
|
321 |
+
5. Choose to:
|
322 |
+
- Get alternatives for the first medicine
|
323 |
+
- Analyze the entire prescription for interactions and information
|
324 |
+
|
325 |
+
### Note:
|
326 |
+
- Your API key is used only for the current session
|
327 |
+
- For best results, ensure the prescription image is clear and readable
|
328 |
+
""")
|
329 |
+
|
330 |
+
with gr.Tab("Medicine Alternative Recommender"):
|
331 |
+
gr.Markdown("## Medicine Alternative Recommender")
|
332 |
+
gr.Markdown("This tool recommends alternative medicines based on an input medicine name using the Together API.")
|
333 |
+
|
334 |
+
with gr.Row():
|
335 |
+
with gr.Column():
|
336 |
+
medicine_name = gr.Textbox(
|
337 |
+
label="Medicine Name",
|
338 |
+
placeholder="Enter a medicine name (e.g., Augmentin 625 Duo)"
|
339 |
+
)
|
340 |
+
submit_btn = gr.Button("Get Recommendations", variant="primary")
|
341 |
+
|
342 |
+
with gr.Column():
|
343 |
+
recommendation_output = gr.Markdown()
|
344 |
+
|
345 |
+
submit_btn.click(
|
346 |
+
recommend_medicine,
|
347 |
+
inputs=[api_key_input, medicine_name, csv_file_input],
|
348 |
+
outputs=recommendation_output
|
349 |
+
)
|
350 |
+
|
351 |
+
gr.Markdown("""
|
352 |
+
## How to use this tool:
|
353 |
+
1. Enter your Together API key (same key used across the application)
|
354 |
+
2. Enter a medicine name - the AI will find it or match similar alternatives
|
355 |
+
3. Click "Get Recommendations" to see alternatives
|
356 |
+
|
357 |
+
### Features:
|
358 |
+
- Even if the exact medicine isn't in the database, the AI will try to find similar alternatives
|
359 |
+
- The system analyzes the medicine name to determine its likely purpose and composition
|
360 |
+
- Recommendations include substitutes, side effects, and usage information
|
361 |
+
""")
|
362 |
+
|
363 |
+
gr.Markdown("""
|
364 |
+
## About This Application
|
365 |
+
|
366 |
+
This Medicine Assistant application combines powerful tools powered by Large Language Models:
|
367 |
+
|
368 |
+
1. **Prescription Medicine Extractor**: Uses computer vision AI to identify medicine names from prescription images
|
369 |
+
2. **Medicine Alternative Recommender**: Provides detailed information about alternative medications
|
370 |
+
3. **Prescription Analyzer**: Analyzes entire prescriptions for potential interactions and insights
|
371 |
+
|
372 |
+
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.
|
373 |
+
|
374 |
+
### Important Note
|
375 |
+
|
376 |
+
This application is for informational purposes only. Always consult with a healthcare professional before making any changes to your medication regimen.
|
377 |
+
""")
|
378 |
+
|
379 |
+
# Launch the app
|
380 |
+
if __name__ == "__main__":
|
381 |
+
app.launch()
|