wip

README.md

@@ -9,7 +9,7 @@ app_file: app.py
 pinned: true
 license: mit
 inference_file: weather_clothing_suggestion.py
-base_model: mistralai/Mistral-7B-Instruct-v0.1
+base_model: mistralai/Mistral-7B-Instruct-v0.3
 pipeline_tag: text-generation
 inference: true
 ---
@@ -39,88 +39,3 @@ To run the Streamlit app locally:
    streamlit run app.py
    ```
 
-## Weather-based Clothing Suggestion Model
-
-This model provides clothing suggestions based on given weather conditions. It uses the Mistral-7B-Instruct model to generate appropriate clothing recommendations.
-
-### API Usage
-
-To use this model via the Hugging Face Inference API, send a POST request with weather data in the following format:
-
-```json
-{
-  "weather_data": {
-    "temperature": 20,
-    "weather": "Sunny",
-    "description": "clear sky",
-    "humidity": 60,
-    "wind_speed": 5
-  }
-}
-```
-
-The model will return a clothing suggestion based on the provided weather conditions.
-
-### Example
-
-Input:
-```json
-{
-  "weather_data": {
-    "temperature": 20,
-    "weather": "Sunny",
-    "description": "clear sky",
-    "humidity": 60,
-    "wind_speed": 5
-  }
-}
-```
-
-Output:
-```json
-{
-  "clothing_suggestion": "For a sunny day with a temperature of 20°C, clear skies, 60% humidity, and a light breeze of 5 m/s, I would suggest the following outfit:\n\nTop: A light, breathable short-sleeved t-shirt or a casual button-up shirt in a light color to reflect the sun.\n\nBottom: Comfortable khaki shorts or a light pair of jeans, depending on your preference and activities planned for the day.\n\nDon't forget to bring a light jacket or sweater in case the temperature drops later in the day, especially if you plan to be out in the evening."
-}
-```
-
-### Example Python Code
-
-Here's an example of how to call the model using Python and the `requests` library:
-
-```python
-import requests
-
-API_URL = "https://api-inference.huggingface.co/models/hadirashwan/wear_what_clothing_suggestion"
-headers = {"Authorization": f"Bearer YOUR_HUGGINGFACE_API_TOKEN"}
-
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-
-weather_data = {
-    "temperature": 20,
-    "weather": "Sunny",
-    "description": "clear sky",
-    "humidity": 60,
-    "wind_speed": 5
-}
-
-output = query({"weather_data": weather_data})
-print(output)
-```
-
-## Repository Structure
-
-- `app.py`: The main Streamlit application file
-- `weather_clothing_suggestion.py`: The inference script for the clothing suggestion model
-- `requirements.txt`: List of Python dependencies
-- Other necessary files and assets
-
-## Setup and Deployment
-
-1. Ensure all files are in the root of your Hugging Face repository.
-2. Set up the necessary secrets and environment variables in your Hugging Face space settings.
-3. The Space will automatically deploy the Streamlit app based on the `app_file` specified in the YAML header.
-4. The model inference will use the script specified by `inference_file` in the YAML header.
-
-For any issues or suggestions, please open an issue in this repository.

app.py

@@ -18,7 +18,6 @@ SUPABASE_KEY =  os.getenv("SUPABASE_KEY")
 
 # Initialize the Hugging Face Inference Client
 
-llvm_model_url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3/v1/chat/completions"
 
 
 # Initialize Supabase
@@ -27,6 +26,7 @@ supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
 model = SentenceTransformer('thenlper/gte-small')
 
 def call_llvm_model(prompt):
+    llvm_model_url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3/v1/chat/completions"
     payload = {
     "model": "mistralai/Mistral-7B-Instruct-v0.3",
     "messages": [
@@ -101,8 +101,18 @@ def get_ai_weather_explanation(weather_data):
     return call_llvm_model(prompt)
 
 def get_relevant_quote(weather_condition):
-    # Encode the weather condition
-    weather_embedding = model.encode(weather_condition).tolist()
+
+    url = "https://api-inference.huggingface.co/models/mixedbread-ai/mxbai-embed-large-v1"
+
+    payload = { "inputs": weather_condition}
+    headers = {
+    "content-type": "application/json",
+    "Authorization": f"Bearer {HF_API_KEY}"
+    }
+
+    response = requests.post(url, json=payload, headers=headers)
+
+    weather_embedding = response.json()
 
     response =  supabase.rpc("match_quote_embeddings",{
             'query_embedding': weather_embedding,

create_table.sql

@@ -5,5 +5,5 @@ CREATE TABLE public.quote_embeddings (
     chunk_id TEXT,
     quote_number TEXT,
     quote_text TEXT,
-    embedding VECTOR(384)
+    embedding VECTOR(1024)
 );

matching_documents.sql

@@ -1,4 +1,4 @@
-create or replace function match_handbook_docs (
+create or replace function match_quote_embeddings (
   query_embedding vector(1024),
   match_threshold float,
   match_count int
@@ -11,11 +11,11 @@ returns table (
 language sql stable
 as $$
   select 
-    handbook_docs.id,
-    handbook_docs.content,
-    1 - (handbook_docs.embedding <=> query_embedding) as similarity
-  from handbook_docs
-  where 1 - (handbook_docs.embedding <=> query_embedding) > match_threshold
-  order by (handbook_docs.embedding <=> query_embedding) asc
+    quote_embeddings.id,
+    quote_embeddings.quote_text,
+    1 - (quote_embeddings.embedding <=> query_embedding) as similarity
+  from quote_embeddings
+  where 1 - (quote_embeddings.embedding <=> query_embedding) > match_threshold
+  order by (quote_embeddings.embedding <=> query_embedding) asc
   limit match_count;
 $$;

setup_db.py

@@ -2,7 +2,6 @@ import os
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from sentence_transformers import SentenceTransformer
 from supabase import create_client, Client
-from postgrest.exceptions import APIError
 
 # Load environment variables
 from dotenv import load_dotenv
@@ -14,7 +13,7 @@ supabase_key = os.getenv("SUPABASE_KEY")
 supabase: Client = create_client(supabase_url, supabase_key)
 
 # Initialize SentenceTransformer
-model = SentenceTransformer('thenlper/gte-small')
+model = SentenceTransformer('mixedbread-ai/mxbai-embed-large-v1')
 
 def process_text_file(file_path: str):
     # Read the file

weather_clothing_suggestion.py

@@ -1,9 +1,30 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+import requests
+import os
+HF_API_KEY = os.getenv("HUGGINGFACE_API_KEY",)
+
+def call_llvm_model(prompt):
+    llvm_model_url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3/v1/chat/completions"
+    payload = {
+    "model": "mistralai/Mistral-7B-Instruct-v0.3",
+    "messages": [
+        {
+        "role": "user",
+        "content": prompt,
+        }
+    ],
+    "max_tokens": 500,
+    "stream": False
+    }
+    headers = {
+    "Authorization": f"Bearer {HF_API_KEY}",
+    "content-type": "application/json"
+    }
+
+    response = requests.post(llvm_model_url, json=payload, headers=headers)
+
+    response = response.json()
+    return response['choices'][0]['message']['content']
 
-# Load model and tokenizer
-model_name = "mistralai/Mistral-7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
 
 def generate_clothing_suggestion(weather_data):
     prompt = f"""
@@ -16,11 +37,7 @@ def generate_clothing_suggestion(weather_data):
     Suggest appropriate clothing to wear, including top and bottom.
     """
 
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7, top_k=50, top_p=0.95)
-    suggestion = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    return suggestion.split(prompt)[-1].strip()
+    return call_llvm_model(prompt)
 
 def inference(weather_data):
     try: