Update app.py

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form
 import google.generativeai as genai
 from PIL import Image
 import io
@@ -6,6 +6,7 @@ import json
 import re
 import os
 import uvicorn
+from enum import Enum
 
 app = FastAPI()
 
@@ -15,17 +16,229 @@ if not api_key:
     raise RuntimeError("GOOGLE_API_KEY is not set in the environment variables.")
 genai.configure(api_key=api_key)
 
+class AnalysisType(str, Enum):
+    DETECT_ALLERGENS = "detect_allergens"
+    DETAILED_NUTRIENT_INFO = "detailed_nutrient_info"
+    LEARN_ABOUT_FOOD = "learn_about_food"
+    FUN_FACTS = "fun_facts"
+    GENERATE_DISH = "generate_dish"
+
 @app.get("/")
 def read_root():
-    return {"message": "FastAPI is running on Render!"}
+    return {"message": "Food Analysis API is running!"}
 
 @app.head("/")
 async def root_head():
     return {}  # Empty response for HEAD requests
 
+@app.post("/analyze-food")
+async def analyze_food_image(
+    file: UploadFile = File(...),
+    analysis_type: AnalysisType = Form(...)
+):
+    """Analyzes food items in the image based on the selected analysis type"""
+    
+    # Read the uploaded image file
+    image_bytes = await file.read()
+    img = Image.open(io.BytesIO(image_bytes))
+
+    # Load the generative model
+    model = genai.GenerativeModel("gemini-1.5-flash")  # Optimized for real-time image processing
+
+    # Define prompts based on analysis type
+    prompts = {
+        AnalysisType.DETECT_ALLERGENS: """
+        Identify all food items in the given image and list potential allergens.
+        Return the response in valid JSON format following this structure:
+        
+        ```json
+        {
+          "detected_food_items": [
+            {
+              "food_item": "Detected food name",
+              "quantity": "Approximate quantity",
+              "potential_allergens": ["allergen1", "allergen2"],
+              "allergen_severity": {
+                "allergen1": "high/medium/low",
+                "allergen2": "high/medium/low"
+              },
+              "common_cross_reactivity": ["other allergen1", "other allergen2"]
+            }
+          ]
+        }
+        ```
+        
+        Ensure the response is valid JSON inside triple backticks.
+        """,
+        
+        AnalysisType.DETAILED_NUTRIENT_INFO: """
+        Identify all food items in the given image and provide comprehensive nutritional information.
+        Return the response in valid JSON format following this structure:
+        
+        ```json
+        {
+          "detected_food_items": [
+            {
+              "food_item": "Detected food name",
+              "quantity": "Approximate quantity",
+              "nutritional_info": {
+                "calories_kcal": value,
+                "protein_g": value,
+                "carbohydrates_g": value,
+                "fat_g": value,
+                "fiber_g": value,
+                "sugar_g": value,
+                "sodium_mg": value,
+                "potassium_mg": value,
+                "calcium_mg": value,
+                "iron_mg": value,
+                "vitamins": {
+                  "Vitamin A_mcg": value,
+                  "Vitamin C_mg": value,
+                  "Vitamin D_mcg": value,
+                  "Vitamin E_mg": value,
+                  "Vitamin K_mcg": value,
+                  "Vitamin B1_mg": value,
+                  "Vitamin B2_mg": value,
+                  "Vitamin B3_mg": value,
+                  "Vitamin B6_mg": value,
+                  "Vitamin B12_mcg": value,
+                  "Folate_mcg": value
+                },
+                "minerals": {
+                  "Magnesium_mg": value,
+                  "Zinc_mg": value,
+                  "Selenium_mcg": value,
+                  "Phosphorus_mg": value
+                }
+              },
+              "glycemic_index": value,
+              "macronutrient_ratio": {
+                "protein_percent": value,
+                "carbs_percent": value,
+                "fat_percent": value
+              }
+            }
+          ],
+          "total_meal_nutrition": {
+            "calories_kcal": value,
+            "protein_g": value,
+            "carbohydrates_g": value,
+            "fat_g": value
+          }
+        }
+        ```
+        
+        Ensure the response is valid JSON inside triple backticks.
+        """,
+        
+        AnalysisType.LEARN_ABOUT_FOOD: """
+        Identify all food items in the given image and provide educational information about them.
+        Return the response in valid JSON format following this structure:
+        
+        ```json
+        {
+          "detected_food_items": [
+            {
+              "food_item": "Detected food name",
+              "origin": "Geographic origin of the food",
+              "cultural_significance": "Brief description of cultural importance",
+              "history": "Brief history of the food",
+              "preparation_methods": ["method1", "method2"],
+              "key_ingredients": ["ingredient1", "ingredient2"],
+              "nutritional_highlights": ["highlight1", "highlight2"],
+              "health_benefits": ["benefit1", "benefit2"],
+              "interesting_facts": ["fact1", "fact2"]
+            }
+          ]
+        }
+        ```
+        
+        Ensure the response is valid JSON inside triple backticks.
+        """,
+        
+        AnalysisType.FUN_FACTS: """
+        Identify all food items in the given image and provide fun and interesting facts about them.
+        Return the response in valid JSON format following this structure:
+        
+        ```json
+        {
+          "detected_food_items": [
+            {
+              "food_item": "Detected food name",
+              "fun_facts": [
+                "Fun fact 1 about this food",
+                "Fun fact 2 about this food",
+                "Fun fact 3 about this food"
+              ],
+              "did_you_know": "An interesting surprising fact",
+              "world_records": ["Any world records related to this food"],
+              "pop_culture_references": ["How this food appears in movies, TV, etc."],
+              "weird_traditions": ["Strange traditions involving this food"]
+            }
+          ]
+        }
+        ```
+        
+        Ensure the response is valid JSON inside triple backticks.
+        """,
+        
+        AnalysisType.GENERATE_DISH: """
+        Identify all food items in the given image and suggest creative dishes that can be made with them.
+        Return the response in valid JSON format following this structure:
+        
+        ```json
+        {
+          "detected_ingredients": ["ingredient1", "ingredient2"],
+          "suggested_dishes": [
+            {
+              "dish_name": "Creative dish name",
+              "cuisine_type": "Type of cuisine",
+              "difficulty_level": "easy/medium/hard",
+              "preparation_time_minutes": value,
+              "ingredients": {
+                "from_image": ["ingredient1", "ingredient2"],
+                "additional_needed": ["extra1", "extra2"]
+              },
+              "recipe_steps": [
+                "Step 1 description",
+                "Step 2 description"
+              ],
+              "nutritional_highlights": ["highlight1", "highlight2"],
+              "serving_suggestions": ["suggestion1", "suggestion2"]
+            }
+          ]
+        }
+        ```
+        
+        Ensure the response is valid JSON inside triple backticks.
+        """
+    }
+
+    # Get the appropriate prompt
+    prompt = prompts.get(analysis_type)
+    if not prompt:
+        raise HTTPException(status_code=400, detail="Invalid analysis type")
+
+    # Get response from API
+    response = model.generate_content([prompt, img])
+
+    # Extract JSON response
+    try:
+        # Use regex to extract JSON block
+        match = re.search(r'```json\s*(\{.*?\})\s*```', response.text, re.DOTALL)
+        if match:
+            json_response = match.group(1)  # Extract JSON content
+            return json.loads(json_response)  # Convert to Python dictionary
+        else:
+            return {"error": "Response does not contain valid JSON format", "raw_response": response.text}
+    except json.JSONDecodeError:
+        return {"error": "Failed to parse JSON response", "raw_response": response.text}
+
+# Keep the original analyze endpoint for backward compatibility
 @app.post("/analyze")
-async def analyze_food_image(file: UploadFile = File()):
-    """Analyzes food items in the image using Gemini 1.5 Flash"""
+async def analyze_food_image_original(file: UploadFile = File()):
+    """Analyzes food items in the image using Gemini 1.5 Flash (original endpoint)"""
     
     # Read the uploaded image file
     image_bytes = await file.read()
@@ -79,10 +292,7 @@ async def analyze_food_image(file: UploadFile = File()):
     """
 
     # Get response from API
-    response = model.generate_content([prompt, img])  # Pass image correctly
-
-    # Debugging: Print the raw response
-    print(f"Raw Response from Gemini: {response}")
+    response = model.generate_content([prompt, img])
 
     # Extract JSON response
     try:
@@ -99,4 +309,4 @@ async def analyze_food_image(file: UploadFile = File()):
 # Entry point for Render deployment
 if __name__ == "__main__":
     port = int(os.getenv("PORT", 7860))  # Render assigns PORT dynamically
-    uvicorn.run(app, host="0.0.0.0", port=port)
+    uvicorn.run(app, host="0.0.0.0", port=port)