Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+from sentence_transformers import SentenceTransformer
+import torch
+import numpy as np
+import requests
+
+#LLM we are using
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+#adding text file
+with open("be_a_better_you.txt", "r", encoding="utf-8") as file1, open("journal_prompts.txt", "r", encoding="utf-8") as file2, open("workout.txt", "r", encoding="utf-8") as file3:
+    wellness_text = file1.read() + "\n" + file2.read() +"\n" + file3.read()
+
+#cleaning up the text
+cleaned_text = wellness_text.strip()
+chunks = cleaned_text.split("\n")
+cleaned_chunks = []
+
+#putting text in chunks
+for chunk in chunks:
+    stripped_chunk = chunk.strip()
+    if stripped_chunk:
+        cleaned_chunks.append(stripped_chunk)
+
+#import model for embeddings
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
+
+def get_top_chunks(query):
+    # creating a function taking query as my parameter
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    # encode query to vector embedding for comparison
+    query_embedding_normalized = query_embedding / query_embedding.norm()
+    # normalize query to 1: allows for comparison of meaning
+    chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+    # normalizing chunks for comparison of meaning
+    similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
+    # using matmul (matrix multiplication) method to compare query to chunks
+    top_indices = torch.topk(similarities, k=3).indices
+    # get the indices of the chunks thart are most similar to my query
+
+    top_chunks = []
+
+    for i in top_indices:
+        chunk = cleaned_chunks[i]
+    # for each index number in top_indices, get back the text
+        top_chunks.append(chunk)
+
+    return top_chunks
+
+def get_nutrition_info(food_query):
+    url = "https://trackapi.nutritionix.com/v2/natural/nutrients"
+    headers = {
+        "x-app-id": "5e0843",
+        "x-app-key": "00948483e0e3540ea8dc5297ad8216d9",
+        "Content-Type": "application/json"
+    }
+    body = {
+        "query": food_query
+    }
+    response = requests.post(url, headers=headers, json=body)
+    if response.status_code == 200:
+        data = response.json()
+        return data
+    else:
+        return None
+
+
+def get_motivational_quote():
+    url = "https://zenquotes.io/api/random"
+    response = requests.get(url)
+    if response.status_code == 200:
+        quote = response.json()['q'] + " -" + response.json()[0]['a']
+        return quote
+    else:
+        return "Keep going! You're doing great."
+
+
+
+def respond(message, history):
+    messages = [{
+        "role": "system", 
+        "content": "You are a big sister chatbot named Nessie. You help people feel better in a simple manner. Always reply in 3 sentences or less. Do NOT stop mid-sentence, as you may confuse them even more. When the user is asking for ideas give 5 max, as you don't want to overwhelm them"
+    }]
+    # change the personality
+    context = get_top_chunks(message)
+    
+    if history:
+        messages.extend(history)
+    messages.append({"role": "user", "content": message})
+
+    user_context = f"{message}\nInformation: {context}"
+    messages.append({"role": "user", "content": user_context})
+    response = ""
+    for messages in client.chat_completion(
+        messages,
+        max_tokens = 200,
+        stream = True,
+    ):
+        token = messages.choices[0].delta.content
+        response+= token 
+        yield response
+
+theme = gr.themes.Soft(
+    primary_hue="rose",
+    secondary_hue="zinc",
+    neutral_hue="pink",
+)
+
+with gr.Blocks(theme=theme) as demo:
+    chatbot = gr.ChatInterface(
+        fn=respond,
+        type='messages',
+        title="Hi! I'm Nessie, your personal wellness assistant. What can I assist you with today?",
+        examples=[
+            "Can you help me with my dietary goals? I want to track my calories, macros, and get advice based on myself.",
+            "Can you help me reach my fitness goals? I would like guidance and recommendations on workouts based on my goals.",
+            "Can you give me some journal prompts? I want to start journaling to help myself reflect on my goals and have some daily affirmations. "
+        ]
+    )
+    #chatbot = gr.ChatInterface(respond, type = 'messages', title= "Hi! I'm Nessie, your personal wellness assistant. What can I assist you with today?",examples=["Can I help you with your dietary goals? I can help you track your calories, macros, and give advice based on personal goals, height, and weight.","Can I help you with your physical health and help you reach your fitness goals? I can give guidance and recommendations for specific workouts based on personal goals.","If you are struggling, I am here. You are so beautiful and so loved! I'm here for whatever you need. "])
+    demo.launch(debug=True)