Update app.py

app.py

@@ -61,7 +61,8 @@ def extract_medicines(api_key, image):
 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
+    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
@@ -73,14 +74,36 @@ def recommend_medicine(api_key, medicine_name, csv_file=None):
                 df = pd.read_csv(csv_file.name)
         else:
             # Use the default medicine_dataset.csv in the current directory
-            df = pd.read_csv("medicine_dataset.csv")
+            try:
+                df = pd.read_csv("medicine_dataset.csv")
+            except FileNotFoundError:
+                return "Error: Default medicine_dataset.csv not found. Please upload a CSV file."
         
-        # Verify the medicine name exists in the dataset
-        if medicine_name not in df['name'].values:
-            return f"Error: Medicine '{medicine_name}' not found in the dataset. Please check the spelling or try another medicine."
+        # Check if medicine is in the dataset
+        medicine_exists = medicine_name in df['name'].values
         
-        # Create system prompt with CSV data and column structure information
-        system_prompt = f"""Develop an expert system to recommend alternative medicines for {medicine_name} based on the medicine dataset. The dataset has the following columns:
+        # 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
@@ -90,47 +113,28 @@ def recommend_medicine(api_key, medicine_name, csv_file=None):
 - Therapeutic Class: The therapeutic classification
 - Action Class: How the medicine works
 
-Your task is to:
-1. Find the row in the dataset where name matches exactly "{medicine_name}"
-2. Find alternatives by:
-   - Using the substitute0-substitute4 values as primary alternatives
-   - Finding other medicines with similar Chemical Class, Therapeutic Class, or Action Class
+YOUR TASK:
 
-For each recommended alternative, provide:
-- Name of the alternative medicine
-- All side effects (from relevant sideEffect columns)
-- All uses (from relevant use columns)
-- Chemical Class, Habit Forming status, Therapeutic Class, and Action Class
-- A similarity score (0-1) indicating how similar it is to the original medicine
+{"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:"}
 
-Format the response clearly with headings for "Recommended Medicines", "Medicine Details", and "Similarity Score".
-"""
+{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"}
 
-        # Extract the specific row containing the medicine data to give more context
-        medicine_data = df[df['name'] == medicine_name]
-        if not medicine_data.empty:
-            # Convert the specific medicine data to a string representation
-            medicine_info = medicine_data.to_string(index=False)
-            system_prompt += f"\n\nThe specific data for {medicine_name} is:\n{medicine_info}\n\n"
-            
-            # Extract substitute information for better recommendations
-            substitutes = []
-            for i in range(5):  # substitute0 through substitute4
-                col_name = f"substitute{i}"
-                if col_name in medicine_data.columns:
-                    sub_value = medicine_data[col_name].iloc[0]
-                    if pd.notna(sub_value) and sub_value != "":
-                        substitutes.append(sub_value)
-            
-            if substitutes:
-                system_prompt += f"The primary substitutes for {medicine_name} are: {', '.join(substitutes)}\n\n"
-        
-        # Include a sample of other medicines for comparison
-        other_medicines = df[df['name'] != medicine_name].sample(min(10, len(df)-1)) if len(df) > 1 else pd.DataFrame()
-        if not other_medicines.empty:
-            system_prompt += "Here's a sample of other medicines in the dataset for comparison:\n"
-            for idx, row in other_medicines.iterrows():
-                system_prompt += f"- {row['name']}: Chemical Class: {row['Chemical Class']}, Therapeutic Class: {row['Therapeutic Class']}, Action Class: {row['Action Class']}\n"
+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)
@@ -145,14 +149,20 @@ Format the response clearly with headings for "Recommended Medicines", "Medicine
                 },
                 {
                     "role": "user",
-                    "content": f"Please recommend alternatives for {medicine_name} based on the dataset. Include detailed information about each alternative."
+                    "content": f"Please recommend alternatives for {medicine_name} based on the available information."
                 }
             ],
-            max_tokens=2000
+            max_tokens=2000,
+            temperature=0.7  # Slightly higher temperature for creative recommendations
         )
         
-        # Return the generated recommendations
-        return response.choices[0].message.content
+        # 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)}"
@@ -181,10 +191,77 @@ def send_medicine_to_recommender(api_key, medicine_names, csv_file):
     # Call the recommend medicine function with the first extracted medicine
     return recommend_medicine(api_key, first_medicine, csv_file)
 
-# Create Gradio interface with tabs for both functionalities
+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 and find alternative medicines.")
+    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(
@@ -194,11 +271,10 @@ with gr.Blocks(title="Medicine Assistant") as app:
     )
     
     # Create a file input for CSV that can be shared between tabs
-    # Fixed the 'type' parameter to use 'filepath' instead of 'file'
     csv_file_input = gr.File(
         label="Upload Medicine CSV (Optional)", 
         file_types=[".csv"],
-        type="filepath"  # Changed from 'file' to 'filepath'
+        type="filepath"  
     )
     gr.Markdown("If no CSV is uploaded, the app will use the default 'medicine_dataset.csv' file.")
     
@@ -211,11 +287,22 @@ with gr.Blocks(title="Medicine Assistant") as app:
                 with gr.Column():
                     image_input = gr.Image(type="filepath", label="Upload Prescription Image")
                     extract_btn = gr.Button("Extract Medicines")
-                    recommend_from_extract_btn = gr.Button("Get Recommendations for First Medicine")
                     
                 with gr.Column():
                     extracted_output = gr.Textbox(label="Extracted Medicines", lines=10)
-                    recommendation_from_extract_output = gr.Markdown(label="Recommendations")
+            
+            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(
@@ -230,13 +317,21 @@ with gr.Blocks(title="Medicine Assistant") as app:
                 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. Click 'Get Recommendations for First Medicine' to find alternatives
+            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
@@ -251,12 +346,12 @@ with gr.Blocks(title="Medicine Assistant") as app:
                 with gr.Column():
                     medicine_name = gr.Textbox(
                         label="Medicine Name", 
-                        placeholder="Enter a medicine name exactly as it appears in the dataset"
+                        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(label="Recommendations")
+                    recommendation_output = gr.Markdown()
             
             submit_btn.click(
                 recommend_medicine,
@@ -267,30 +362,25 @@ with gr.Blocks(title="Medicine Assistant") as app:
             gr.Markdown("""
             ## How to use this tool:
             1. Enter your Together API key (same key used across the application)
-            2. Enter a medicine name **exactly as it appears** in the CSV file
+            2. Enter a medicine name - the AI will find it or match similar alternatives
             3. Click "Get Recommendations" to see alternatives
             
-            ### CSV Format Requirements:
-            The app expects a CSV with these 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
+            ### 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 two powerful tools:
+    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
     
-    Both 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.
+    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