Update app.py

app.py

@@ -10,8 +10,25 @@ from fastapi.responses import HTMLResponse
 import os
 import base64
 from groq import Groq
+import faiss
+import pickle
+import torch
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+
+# Load the FAISS index
+index = faiss.read_index("knowledge_base.faiss")
+
+# Load the titles metadata
+with open("titles.pkl", "rb") as f:
+    titles = pickle.load(f)
+
+# Load CLIP model and processor on CPU
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to("cpu")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
 # Initialize Groq client
-client = Groq(api_key='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')
+client = Groq(api_key='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')
 
 # MongoDB connection setup
 def get_mongo_client():
@@ -56,17 +73,38 @@ class Recipe(BaseModel):
     directions: List[str]
 
 
+class get_recipe_name(BaseModel):
+    recipe_name: List[str]
+    ingredients: List[List[str]]
+
 # Data model for LLM to generate
 class Alternative_Ingredient(BaseModel):
     name: str
     quantity: str
 
-
 class Alternative_Recipe(BaseModel):
     recipe_name: str
     alternative_ingredients: List[Alternative_Ingredient]
     alternative_directions: List[str]
 
+# Function for finding the most similar image
+def find_similar_image(image_path, threshold=30.0):
+    # Load and preprocess the input image
+    image = Image.open(image_path).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")  
+
+    # Generate embedding for the input image on CPU
+    with torch.no_grad():
+        image_features = model.get_image_features(**inputs).numpy()  # No need for .cpu()
+
+    # Perform similarity search in FAISS
+    distances, indices = index.search(image_features, k=1)  # Search for the most similar embedding
+
+    # Check if the closest match meets the threshold
+    if distances[0][0] < threshold:
+        return titles[indices[0][0]]
+    else:
+        return "Not Found"
 def get_recipe(recipe_name: str) -> Recipe:
     chat_completion = client.chat.completions.create(
         messages=[
@@ -81,7 +119,7 @@ def get_recipe(recipe_name: str) -> Recipe:
                 "content": f"Fetch a recipe for {recipe_name}",
             },
         ],
-        model="llama-3.2-90b-text-preview",
+        model="llama3-8b-8192",
         temperature=0,
         # Streaming is not supported in JSON mode
         stream=False,
@@ -91,9 +129,6 @@ def get_recipe(recipe_name: str) -> Recipe:
     return Recipe.model_validate_json(chat_completion.choices[0].message.content)
 
 
-
-
-
 def Suggest_ingredient_alternatives(recipe_name: str, dietary_restrictions: str, allergies: List) -> Alternative_Recipe:
     chat_completion = client.chat.completions.create(
         messages=[
@@ -121,7 +156,7 @@ def Suggest_ingredient_alternatives(recipe_name: str, dietary_restrictions: str,
                 Allergies: {', '.join(allergies)}""",
             },
         ],
-        model="llama-3.2-90b-text-preview",
+        model="llama3-8b-8192",
         temperature=0,
         # Streaming is not supported in JSON mode
         stream=False,
@@ -130,123 +165,8 @@ def Suggest_ingredient_alternatives(recipe_name: str, dietary_restrictions: str,
     )
     return Alternative_Recipe.model_validate_json(chat_completion.choices[0].message.content)
 
-
-def get_status(content):
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "system",
-                "content": """Your are an expert agent to status yes if any kind of recipe dish present in explanation other no
-
-                Json output format:
-                {'status':return'yes' if any dish present in expalantion return 'no' if not dish present in image}
-                """,
-            },
-            {
-                "role": "user",
-                "content": f"Image Explanation {content}",
-            },
-        ],
-        model="llama3-groq-70b-8192-tool-use-preview",
-        temperature=0,
-        # Streaming is not supported in JSON mode
-        stream=False,
-        # Enable JSON mode by setting the response format
-        response_format={"type": "json_object"},
-    )
-    return chat_completion.choices[0].message.content
-
-# Function to encode the image
-def encode_image(image_path):
-  with open(image_path, "rb") as image_file:
-    return base64.b64encode(image_file.read()).decode('utf-8')
-
-def explain_image(base64_image):
-    text_query = '''
-    explain the image.
-    '''
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": text_query},
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{base64_image}",
-                        },
-                    },
-                ],
-
-            }
-        ],
-        model="llama-3.2-90b-vision-preview")
-    return chat_completion.choices[0].message.content
-
-
-class get_recipe_name(BaseModel):
-    recipe_name: List[str]
-    ingredients: List[List[str]]
-
-
-def generate_recipe_name(base64_image):
-    # Example of how the JSON should look to make it clearer
-    example_json_structure = {
-    "recipe_name": "Chicken Karhai",
-    "ingredients": [
-        "chicken",
-        "tomatoes",
-        "onions",
-        "ginger",
-        "garlic",
-        "green chilies",
-        "yogurt",
-        "cumin seeds",
-        "coriander powder",
-        "red chili powder",
-        "turmeric powder",
-        "garam masala",
-        "fresh coriander leaves",
-        "oil",
-        "salt"
-    ]
-}
-
-    # Generating the query prompt to ask for ingredients
-    text_query = f'''What are the ingredients used in these dishes? Do not add any explanation, just write the names of the ingredients in proper JSON according to the following format:
-    The JSON object must follow this schema:
-    {json.dumps(get_recipe_name.model_json_schema(), indent=2)}
-    
-    Example format:
-    {json.dumps(example_json_structure, indent=2)}
-    
-    Write the name of the dish and then list the ingredients used for each recipe, focusing on traditional Pakistani ingredients and terminology.
-    '''
-
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": text_query},
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{base64_image}",
-                        },
-                    },
-                ],
-
-            }
-        ],
-        response_format={"type": "json_object"},
-        model="llama-3.2-90b-vision-preview")
-    return json.loads(chat_completion.choices[0].message.content)
-
 app = FastAPI()
 
-
 @app.post("/get_recipe/{token}")
 async def get_recipe_response(token: str, recipe_user: RecipeData):
     user = user_collection.find_one({"token": token})
@@ -297,18 +217,10 @@ async def upload_image(token: str, file: UploadFile = File(...)):
     with open(file_path, "wb") as buffer:
         buffer.write(await file.read())
 
-    # Getting the base64 string
-    base64_image = encode_image(file_path)
-
-    status = get_status(explain_image(base64_image))
-    status_json = json.loads(status)
-    if status_json['status'].lower() == 'no':
-        response = {"recipe_name": [], 'ingredients': []}
-    else:
-        response = generate_recipe_name(base64_image)
+    result = find_similar_image(file_path, threshold=30.0)
 
     return {
-        "Response": response
+        "Response": result
     }
 
 
@@ -353,3 +265,8 @@ async def check_credentials(user: UserData):
         "last_name": existing_user["last_name"],
         "token": token,
     }
+
+
+@app.get("/")
+async def root():
+    return {"message": "API is up and running!"}