update

app.py

@@ -1,99 +1,30 @@
+
 import gradio as gr
 from huggingface_hub import InferenceClient
-import requests
 from sentence_transformers import SentenceTransformer
 import torch
 
-
-SPOONACULAR_API_KEY = "71259036cfb3405aa5d49c1220a988c5"
-recipe_id_map = {}
-
-client=InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
-
-# # Search recipes
-
-# global recipe_id_map
-url = "https://api.spoonacular.com/recipes/complexSearch"
-params = {
-    # "query": ingredient,
-    # "cuisine": cuisine,
-    # "diet": dietary,
-    "number": 3,
-    "apiKey": SPOONACULAR_API_KEY
-}
-res = requests.get(url, params=params)
-data = res.json()
-
-
-#     if "results" not in data or not data["results"]:
-#         recipe_id_map = {}
-#         return gr.update(choices=[], visible=True, label="No recipes found"), gr.update(value="No recipes found.")
-
-#     recipe_id_map = {r["title"]: r["id"] for r in data["results"]}
-#     return gr.update(choices=list(recipe_id_map.keys()), visible=True), gr.update(value="Select a recipe from the dropdown.")
-
-# # Get recipe details
-# def get_recipe_details(selected_title):
-#     if not selected_title or selected_title not in recipe_id_map:
-#         return "Please select a valid recipe."
-
-#     recipe_id = recipe_id_map[selected_title]
-#     url = f"https://api.spoonacular.com/recipes/{recipe_id}/information"
-#     params = {"apiKey": SPOONACULAR_API_KEY}
-#     res = requests.get(url, params=params)
-#     data = res.json()
-
-#     title = data.get("title", "Unknown Title")
-#     time = data.get("readyInMinutes", "N/A")
-#     instructions = data.get("instructions") or "No instructions available."
-#     ingredients_list = data.get("extendedIngredients", [])
-#     ingredients = "\n".join([f"- {item.get('original')}" for item in ingredients_list])
-
-#     return f"### 🍽️ {title}\n**⏱️ Cook Time:** {time} minutes\n\n**📋 Instructions:**\n{instructions}"
-#     gr.Markdown("💬 Go to the next tab to ask our chatbot your questions on the recipe!")
-# # Handle chatbot questions
-# def ask_recipe_bot(message, history):
-#     # Try to find a recipe ID from previous dropdown results
-#     if not recipe_id_map:
-#         return "Please use the dropdown tab first to search for a recipe."
-
-#     # Use the first recipe ID from the map
-#     recipe_id = list(recipe_id_map.values())[0]
-#     url = f"https://api.spoonacular.com/recipes/{recipe_id}/nutritionWidget.json"
-#     params = {"apiKey": SPOONACULAR_API_KEY}
-#     res = requests.get(url, params=params)
-    
-#     if res.status_code != 200:
-#         return "Sorry, I couldn't retrieve nutrition info."
-
-#     data = res.json()
-#     calories = data.get("calories", "N/A")
-#     carbs = data.get("carbs", "N/A")
-#     protein = data.get("protein", "N/A")
-#     fat = data.get("fat", "N/A")
-
-#     if "calorie" in message.lower():
-#         return f"This recipe has {calories}."
-#     elif "protein" in message.lower():
-#         return f"It contains {protein}."
-#     elif "carb" in message.lower():
-#         return f"It has {carbs}."
-#     elif "fat" in message.lower():
-#         return f"The fat content is {fat}."
-#     elif "scale" in message.lower() or "double" in message.lower():
-#         return "You can scale ingredients by multiplying each quantity. For example, to double the recipe, multiply every amount by 2."
-#     elif "substitute" in message.lower():
-#         return "Let me know the ingredient you'd like to substitute, and I’ll try to help!"
-#     else:
-#         return "You can ask about calories, protein, carbs, fat, substitutes, or scaling tips."
-
-def respond(message, history, ingredient, cuisine, diet):
-
-    context = search_recipes(ingredient, cuisine, diet)
+# Load knowledge
+with open("recipedataset.txt", "r", encoding="utf-8") as file:
+    knowledge = file.read()
+cleaned_chunks = [chunk.strip() for chunk in knowledge.strip().split("\n") if chunk.strip()]
+model = SentenceTransformer('all-MiniLM-L6-v2')
+chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
+def get_top_chunks(query):
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    query_embedding_normalized = query_embedding / query_embedding.norm()
+    similarities = torch.matmul(chunk_embeddings, query_embedding_normalized)
+    top_indices = torch.topk(similarities, k=5).indices.tolist()
+    return [cleaned_chunks[i] for i in top_indices]
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+def respond(message, history):
+    response = ""
+    top_chunks = get_top_chunks(message)
+    context = "\n".join(top_chunks)
     messages = [
         {
             "role": "system",
-            "content": f"You give recipes from {context}."
+            "content": f"You are a friendly chatbot that responds to the user with this context {context}"
         }
     ]
     if history:
@@ -109,38 +40,11 @@ def respond(message, history, ingredient, cuisine, diet):
         token = message.choices[0].delta.content
         if token is not None:
             response += token
-            yield response 
-
-
-# # Gradio layout
-with gr.Blocks() as demo:
-    gr.Markdown("## 🧠🍴 The BiteBot")
-
-    with gr.Tabs():
-        # with gr.Tab("Search Recipes"):
-            with gr.Row():
-                ingredient = gr.Textbox(label="Preferred Ingredient")
-                cuisine = gr.Textbox(label="Preferred Cuisine")
-                diet = gr.Textbox(label="Dietary Restrictions")
-
-            # search_button = gr.Button("Search Recipes")
-            # recipe_dropdown = gr.Dropdown(label="Select a recipe", visible=False)
-            # recipe_output = gr.Markdown()
-
-            # search_button.click(
-            #     fn=search_recipes,
-            #     inputs=[ingredient, cuisine, diet],
-            #     outputs=[recipe_dropdown, recipe_output]
-            # )
-
-            # recipe_dropdown.change(
-            #     fn=get_recipe_details,
-            #     inputs=recipe_dropdown,
-            #     outputs=recipe_output
-            # )
-
-        # with gr.Tab("Ask BiteBot"):
-            chatbot = gr.ChatInterface(fn=respond, additional_inputs = [ingredient, cuisine, diet], type="messages")
-            gr.Markdown("💬 Ask about calories, macros, scaling, or substitutions. (Run a recipe search first!)")
+            yield response
+with gr.Blocks() as chatbot:
+                    gr.ChatInterface(
+                        fn=respond,
+                        type="messages",
+                    )
+chatbot.launch()
 
-demo.launch()