Update app.py

app.py

@@ -33,122 +33,21 @@ except:
 # Load environment variables
 load_dotenv()
 
-# Import and configure Gemini API
-try:
-    import google.generativeai as genai
-    # Configure Gemini API
-    GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") or GEMINI_API_KEY
-    if GEMINI_API_KEY:
-      genai.configure(api_key=GEMINI_API_KEY)
-      print("Gemini API configured successfully")
-    else:
-      print("Warning: No Gemini API key found. Will use fallback analysis.")
-except ImportError:
-    print("Google Generative AI package not found, using dummy implementation")
-    genai = None
+# Mistral API Key
+MISTRAL_API_KEY = "GlrVCBWyvTYjWGKl5jqtK4K41uWWJ79F"
 
-# 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."
-        
-        # Image preprocessing for better OCR
-        import cv2
-        import numpy as np
-        
-        # Convert PIL image to OpenCV format
-        img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-        
-        # Convert to grayscale
-        gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
-        
-        # Apply thresholding to get black and white image
-        _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
-        
-        # Noise removal
-        kernel = np.ones((1, 1), np.uint8)
-        binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
-        
-        # Dilate to connect text
-        binary = cv2.dilate(binary, kernel, iterations=1)
-        
-        # Convert back to PIL image for tesseract
-        binary_pil = Image.fromarray(cv2.bitwise_not(binary))
-        
-        # Run OCR with improved configuration
-        custom_config = r'--oem 3 --psm 6 -l eng'
-        text = pytesseract.image_to_string(binary_pil, config=custom_config)
-        
-        if not text.strip():
-            # Try original image as fallback
-            text = pytesseract.image_to_string(image, config=custom_config)
-            
-        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)}"
-# Function to parse ingredients from text
-def parse_ingredients(text):
-    if not text:
-        return []
-
-    # Clean up the text
-    text = re.sub(r'^ingredients:?\s*', '', text.lower(), flags=re.IGNORECASE)
-    
-    # Remove common OCR errors and extraneous characters
-    text = re.sub(r'[|\\/@#$%^&*()_+=]', '', text)
-    
-    # Replace common OCR errors
-    text = re.sub(r'\bngredients\b', 'ingredients', text)
-    
-    # Handle common OCR misreads
-    replacements = {
-        '0': 'o', 'l': 'i', '1': 'i',
-        '5': 's', '8': 'b', 'Q': 'g',
-    }
-    
-    for error, correction in replacements.items():
-        text = text.replace(error, correction)
-    
-    # Split by common ingredient separators
-    ingredients = re.split(r',|;|\n', text)
-    
-    # Clean up each ingredient
-    cleaned_ingredients = []
-    for i in ingredients:
-        i = i.strip().lower()
-        if i and len(i) > 1:  # Ignore single characters which are likely OCR errors
-            cleaned_ingredients.append(i)
-    
-    return cleaned_ingredients
-
-# Function to analyze ingredients with Gemini
-# Function to analyze ingredients with Gemini
-def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
+# Import and configure Mistral API
+def analyze_ingredients_with_mistral(ingredients_list, health_conditions=None):
     """
-    Use Gemini to analyze ingredients and provide health insights
+    Use Mistral AI to analyze ingredients and provide health insights.
     """
     if not ingredients_list:
         return "No ingredients detected or provided."
 
     # Prepare the list of ingredients for the prompt
     ingredients_text = ", ".join(ingredients_list)
-    
-    # Check if Gemini API is available
-    if not genai or not os.getenv("GEMINI_API_KEY"):
-        return dummy_analyze(ingredients_list, health_conditions)
-    
-    # Create a prompt for Gemini
+
+    # Create a prompt for Mistral
     if health_conditions and health_conditions.strip():
         prompt = f"""
         Analyze the following food ingredients for a person with these health conditions: {health_conditions}
@@ -172,39 +71,22 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
         """
 
     try:
-        # First, check available models
-        try:
-            models = genai.list_models()
-            available_models = [m.name for m in models]
-            
-            # Try models in order of preference
-            model_names = ['gemini-pro', 'gemini-1.5-pro', 'gemini-1.0-pro']
-            
-            # Find first available model from our preference list
-            model_name = None
-            for name in model_names:
-                if any(name in m for m in available_models):
-                    model_name = name
-                    break
-                    
-            # If none of our preferred models are available, use the first available model
-            if not model_name and available_models:
-                model_name = available_models[0]
-                
-            if not model_name:
-                return dummy_analyze(ingredients_list, health_conditions) + "\n\n(Using fallback analysis: No available models found)"
-                
-            model = genai.GenerativeModel(model_name)
-            response = model.generate_content(prompt)
-            
-            # Check if response is valid
-            if hasattr(response, 'text') and response.text:
-                analysis = response.text
-            else:
-                return dummy_analyze(ingredients_list, health_conditions) + "\n\n(Using fallback analysis: Empty API response)"
-                
-        except Exception as e:
-            return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: {str(e)})"
+        headers = {
+            "Authorization": f"Bearer {MISTRAL_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        data = {
+            "model": "mistral-small",  # Or another suitable model
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": 0.7,
+        }
+
+        response = requests.post("https://api.mistral.ai/v1/chat/completions", headers=headers, json=data)
+
+        if response.status_code == 200:
+            analysis = response.json()['choices'][0]['message']['content']
+        else:
+            return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: Mistral API Error - {response.status_code} - {response.text})"
 
         # Add disclaimer
         disclaimer = """
@@ -218,6 +100,8 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
     except Exception as e:
         # Fallback to basic analysis if API call fails
         return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: {str(e)})"
+
+
 # Dummy analysis function for when API is not available
 def dummy_analyze(ingredients_list, health_conditions=None):
     ingredients_text = ", ".join(ingredients_list)
@@ -227,8 +111,8 @@ def dummy_analyze(ingredients_list, health_conditions=None):
     ## 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.
+    This is a simulated analysis since the Mistral API call failed. In the actual application,
+    the ingredients would be analyzed by Mistral for their health implications.
     ## Health Considerations
     """
 
@@ -249,6 +133,93 @@ def dummy_analyze(ingredients_list, health_conditions=None):
 
     return report
 
+# 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."
+
+        # Image preprocessing for better OCR
+        import cv2
+        import numpy as np
+
+        # Convert PIL image to OpenCV format
+        img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+
+        # Convert to grayscale
+        gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
+
+        # Apply thresholding to get black and white image
+        _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+        # Noise removal
+        kernel = np.ones((1, 1), np.uint8)
+        binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
+
+        # Dilate to connect text
+        binary = cv2.dilate(binary, kernel, iterations=1)
+
+        # Convert back to PIL image for tesseract
+        binary_pil = Image.fromarray(cv2.bitwise_not(binary))
+
+        # Run OCR with improved configuration
+        custom_config = r'--oem 3 --psm 6 -l eng'
+        text = pytesseract.image_to_string(binary_pil, config=custom_config)
+
+        if not text.strip():
+            # Try original image as fallback
+            text = pytesseract.image_to_string(image, config=custom_config)
+
+        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)}"
+
+# Function to parse ingredients from text
+def parse_ingredients(text):
+    if not text:
+        return []
+
+    # Clean up the text
+    text = re.sub(r'^ingredients:?\s*', '', text.lower(), flags=re.IGNORECASE)
+
+    # Remove common OCR errors and extraneous characters
+    text = re.sub(r'[|\\/@#$%^&*()_+=]', '', text)
+
+    # Replace common OCR errors
+    text = re.sub(r'\bngredients\b', 'ingredients', text)
+
+    # Handle common OCR misreads
+    replacements = {
+        '0': 'o', 'l': 'i', '1': 'i',
+        '5': 's', '8': 'b', 'Q': 'g',
+    }
+
+    for error, correction in replacements.items():
+        text = text.replace(error, correction)
+
+    # Split by common ingredient separators
+    ingredients = re.split(r',|;|\n', text)
+
+    # Clean up each ingredient
+    cleaned_ingredients = []
+    for i in ingredients:
+        i = i.strip().lower()
+        if i and len(i) > 1:  # Ignore single characters which are likely OCR errors
+            cleaned_ingredients.append(i)
+
+    return cleaned_ingredients
+
+
 # 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":
@@ -257,9 +228,9 @@ def process_input(input_method, text_input, camera_input, upload_input, health_c
             # 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)
+            return analyze_ingredients_with_mistral(ingredients, health_conditions)
         else:
             return "No camera image captured. Please try again."
 
@@ -269,16 +240,16 @@ def process_input(input_method, text_input, camera_input, upload_input, health_c
             # 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)
+            return analyze_ingredients_with_mistral(ingredients, health_conditions)
         else:
             return "No image uploaded. Please try again."
 
     elif input_method == "Manual Entry":
         if text_input and text_input.strip():
             ingredients = parse_ingredients(text_input)
-            return analyze_ingredients_with_gemini(ingredients, health_conditions)
+            return analyze_ingredients_with_mistral(ingredients, health_conditions)
         else:
             return "No ingredients entered. Please try again."