Update app.py

app.py

@@ -1,11 +1,22 @@
 import os
 import subprocess
 import sys
+import re
+import numpy as np
+from PIL import Image
+import gradio as gr
+import requests
+import json
+from dotenv import load_dotenv
 
-# Instead of hardcoding the path
-pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
+# Attempt to install pytesseract if not found
+try:
+    import pytesseract
+except ImportError:
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'pytesseract'])
+    import pytesseract
 
-# Try this more flexible approach
+# AFTER importing pytesseract, then set the path
 try:
     # First try the default path
     if os.path.exists('/usr/bin/tesseract'):
@@ -18,31 +29,39 @@ try:
 except:
     # If all else fails, try the default installation path
     pytesseract.pytesseract.tesseract_cmd = 'tesseract'
-import gradio as gr
-import re
-import numpy as np
-from PIL import Image
-import pytesseract
-import requests
-import json
-import os
-from dotenv import load_dotenv
-import google.generativeai as genai
 
 # Load environment variables
 load_dotenv()
 
-# Configure Gemini API
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=GEMINI_API_KEY)
-
+# Import and configure Gemini API
+try:
+    import google.generativeai as genai
+    # Configure Gemini API
+    GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+    if GEMINI_API_KEY:
+        genai.configure(api_key=GEMINI_API_KEY)
+except ImportError:
+    print("Google Generative AI package not found, using dummy implementation")
+    genai = None
 
 # Function to extract text from images using OCR
 def extract_text_from_image(image):
     try:
         if image is None:
             return "No image captured. Please try again."
+        
+        # Verify Tesseract executable is accessible
+        try:
+            subprocess.run([pytesseract.pytesseract.tesseract_cmd, "--version"], 
+                          check=True, capture_output=True, text=True)
+        except (subprocess.SubprocessError, FileNotFoundError):
+            return "Tesseract OCR is not installed or not properly configured. Please check installation."
+        
         text = pytesseract.image_to_string(image)
+        
+        if not text.strip():
+            return "No text could be extracted. Ensure image is clear and readable."
+        
         return text
     except Exception as e:
         return f"Error extracting text: {str(e)}"
@@ -76,38 +95,39 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
     if health_conditions and health_conditions.strip():
         prompt = f"""
         Analyze the following food ingredients for a person with these health conditions: {health_conditions}
-
         Ingredients: {ingredients_text}
-
         For each ingredient:
         1. Provide its potential health benefits
         2. Identify any potential risks
         3. Note if it may affect the specified health conditions
-
         Then provide an overall assessment of the product's suitability for someone with the specified health conditions.
         Format your response in markdown with clear headings and sections.
         """
     else:
         prompt = f"""
         Analyze the following food ingredients:
-
         Ingredients: {ingredients_text}
-
         For each ingredient:
         1. Provide its potential health benefits
         2. Identify any potential risks or common allergens associated with it
-
         Then provide an overall assessment of the product's general health profile.
         Format your response in markdown with clear headings and sections.
         """
 
     try:
+        # Check if Gemini API is available
+        if not genai or not os.getenv("GEMINI_API_KEY"):
+            return dummy_analyze(ingredients_list, health_conditions)
+            
         # Call the Gemini API
         model = genai.GenerativeModel('gemini-pro')
         response = model.generate_content(prompt)
 
-        # Extract and return the analysis
-        analysis = response.text
+        # Check if response is valid
+        if hasattr(response, 'text') and response.text:
+            analysis = response.text
+        else:
+            return "Error: Received empty response from analysis service. Please try again."
 
         # Add disclaimer
         disclaimer = """
@@ -122,11 +142,46 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
         # Fallback to basic analysis if API call fails
         return f"Error connecting to analysis service: {str(e)}\n\nPlease try again later."
 
+# Dummy analysis function for when API is not available
+def dummy_analyze(ingredients_list, health_conditions=None):
+    ingredients_text = ", ".join(ingredients_list)
+
+    report = f"""
+    # Ingredient Analysis Report
+    ## Detected Ingredients
+    {", ".join([i.title() for i in ingredients_list])}
+    ## Overview
+    This is a simulated analysis since no API key was provided. In the actual application,
+    the ingredients would be analyzed by an LLM for their health implications.
+    ## Health Considerations
+    """
+
+    if health_conditions:
+        report += f"""
+        The analysis would specifically consider these health concerns: {health_conditions}
+        """
+    else:
+        report += """
+        No specific health concerns were provided, so a general analysis would be performed.
+        """
+
+    report += """
+    ## Disclaimer
+    This analysis is provided for informational purposes only and should not replace professional medical advice.
+    Always consult with a healthcare provider regarding dietary restrictions, allergies, or health conditions.
+    """
+
+    return report
+
 # Function to process input based on method (camera, upload, or manual entry)
 def process_input(input_method, text_input, camera_input, upload_input, health_conditions):
     if input_method == "Camera":
         if camera_input is not None:
             extracted_text = extract_text_from_image(camera_input)
+            # If OCR fails, inform the user they can try manual entry
+            if "Error" in extracted_text or "No text could be extracted" in extracted_text:
+                return extracted_text + "\n\nPlease try using the 'Manual Entry' option instead."
+                
             ingredients = parse_ingredients(extracted_text)
             return analyze_ingredients_with_gemini(ingredients, health_conditions)
         else:
@@ -135,13 +190,17 @@ def process_input(input_method, text_input, camera_input, upload_input, health_c
     elif input_method == "Image Upload":
         if upload_input is not None:
             extracted_text = extract_text_from_image(upload_input)
+            # If OCR fails, inform the user they can try manual entry
+            if "Error" in extracted_text or "No text could be extracted" in extracted_text:
+                return extracted_text + "\n\nPlease try using the 'Manual Entry' option instead."
+                
             ingredients = parse_ingredients(extracted_text)
             return analyze_ingredients_with_gemini(ingredients, health_conditions)
         else:
             return "No image uploaded. Please try again."
 
     elif input_method == "Manual Entry":
-        if text_input.strip():
+        if text_input and text_input.strip():
             ingredients = parse_ingredients(text_input)
             return analyze_ingredients_with_gemini(ingredients, health_conditions)
         else:
@@ -163,7 +222,7 @@ with gr.Blocks(title="AI Ingredient Scanner") as app:
             )
 
             # Camera input
-            camera_input = gr.Image(label="Capture ingredients with camera", type="pil")
+            camera_input = gr.Image(label="Capture ingredients with camera", type="pil", visible=True)
 
             # Image upload
             upload_input = gr.Image(label="Upload image of ingredients label", type="pil", visible=False)
@@ -193,9 +252,9 @@ with gr.Blocks(title="AI Ingredient Scanner") as app:
     # Show/hide inputs based on selection
     def update_visible_inputs(choice):
         return {
-            upload_input: choice == "Image Upload",
-            camera_input: choice == "Camera",
-            text_input: choice == "Manual Entry"
+            upload_input: gr.update(visible=(choice == "Image Upload")),
+            camera_input: gr.update(visible=(choice == "Camera")),
+            text_input: gr.update(visible=(choice == "Manual Entry"))
         }
 
     input_method.change(update_visible_inputs, input_method, [upload_input, camera_input, text_input])
@@ -229,7 +288,6 @@ with gr.Blocks(title="AI Ingredient Scanner") as app:
     2. Take a photo of the ingredients label or enter ingredients manually
     3. Optionally enter your health concerns
     4. Click "Analyze Ingredients" to get your personalized analysis
-
     The AI will automatically analyze the ingredients, their health implications, and their potential impact on your specific health concerns.
     """)
 
@@ -241,7 +299,6 @@ with gr.Blocks(title="AI Ingredient Scanner") as app:
     - Allergies: "peanut allergy" or "shellfish allergy"
     - Dietary restrictions: "vegetarian" or "gluten-free diet"
     - Multiple conditions: "diabetes, high cholesterol, and lactose intolerance"
-
     The AI will tailor its analysis to your specific needs.
     """)
 
@@ -259,56 +316,6 @@ with gr.Blocks(title="AI Ingredient Scanner") as app:
     Always consult with a healthcare provider regarding dietary restrictions, allergies, or health conditions.
     """)
 
-# Function to run when testing without API key
-def run_with_dummy_llm():
-    # Override the LLM function with a dummy version for testing
-    global analyze_ingredients_with_gemini
-
-    def dummy_analyze(ingredients_list, health_conditions=None):
-        ingredients_text = ", ".join(ingredients_list)
-
-        report = f"""
-        # Ingredient Analysis Report
-
-        ## Detected Ingredients
-        {", ".join([i.title() for i in ingredients_list])}
-
-        ## Overview
-        This is a simulated analysis since no API key was provided. In the actual application,
-        the ingredients would be analyzed by an LLM for their health implications.
-
-        ## Health Considerations
-        """
-
-        if health_conditions:
-            report += f"""
-            The analysis would specifically consider these health concerns: {health_conditions}
-            """
-        else:
-            report += """
-            No specific health concerns were provided, so a general analysis would be performed.
-            """
-
-        report += """
-        ## Disclaimer
-        This analysis is provided for informational purposes only and should not replace professional medical advice.
-        Always consult with a healthcare provider regarding dietary restrictions, allergies, or health conditions.
-        """
-
-        return report
-
-    # Replace the real function with the dummy
-    analyze_ingredients_with_gemini = dummy_analyze
-
-    # Launch the app
-    app.launch()
-
 # Launch the app
 if __name__ == "__main__":
-    # Check if API key exists
-    if not os.getenv("GEMINI_API_KEY"):
-        print("WARNING: No Gemini API key found. Running with simulated LLM responses.")
-        run_with_dummy_llm()
-    else:
-        app.launch()
-
+    app.launch()