design

app.py

@@ -1,53 +1,28 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import os
-#importing new libraries
 from sentence_transformers import SentenceTransformer
 import torch
 import numpy as np
 
-# START NEW CODE
-
-# Load and process the knowledge base text file
+# Load and process the knowledge base
 with open("knowledge.txt", "r", encoding="utf-8") as f:
     knowledge_text = f.read()
-
-# Split the text into chunks (for example, by paragraphs)
 chunks = [chunk.strip() for chunk in knowledge_text.split("\n\n") if chunk.strip()]
-
-# Load an embedding model (this one is light and fast)
 embedder = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Precompute embeddings for all chunks (as a tensor for fast similarity search)
 chunk_embeddings = embedder.encode(chunks, convert_to_tensor=True)
 
 def get_relevant_context(query, top_k=3):
-    """
-     Compute the embedding for the query, compare it against all chunk embeddings,
-     and return the top_k most similar chunks concatenated into a context string.
-    """
-
-    # Compute and normalize the query embedding
     query_embedding = embedder.encode(query, convert_to_tensor=True)
     query_embedding = query_embedding / query_embedding.norm()
-
-    # Normalize chunk embeddings along the embedding dimension
     norm_chunk_embeddings = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
-
-    # Compute cosine similarity between the query and each chunk
     similarities = torch.matmul(norm_chunk_embeddings, query_embedding)
-
-    # Get the indices of the top_k most similar chunks
     top_k_indices = torch.topk(similarities, k=top_k).indices.cpu().numpy()
-
-    # Concatenate the top chunks into a single context string
     context = "\n\n".join([chunks[i] for i in top_k_indices])
     return context
 
-## END OF NEW CODE
-
 client = InferenceClient("google/gemma-2-2b-it")
-cycle_ai_prompt = system_prompt = """
+
+cycle_ai_prompt = """
 Cycle-Aware Wellness AI Coach (Strict Enforcement Version)
 ==========================================================
 Mission:
@@ -84,31 +59,43 @@ Core Support Areas (Scope You *Do* Cover):
 - Offer encouragement when users feel overwhelmed, bloated, or inconsistent.
 Final Boundary Rule:
 --------------------
-🔒 Strictly decline all unrelated questions. Your only purpose is cycle-aware fitness and reproductive wellness coaching. Do not give general medical, tech, legal, or life advice.you can still give advice relating to helping symptoms like are homeremeides. no actual medical adivce but recipes are fine
+🔒 Strictly decline all unrelated questions. Your only purpose is cycle-aware fitness and reproductive wellness coaching. Do not give general medical, tech, legal, or life advice. You can still give advice relating to helping symptoms like home remedies. No actual medical advice, but recipes are fine.
 """
+
 def respond(message, history):
     messages = [{"role": "system", "content": cycle_ai_prompt}]
-    #new line of code
     if history:
         for user_msg, assistant_msg in history:
             messages.append({"role": "user", "content": user_msg})
             messages.append({"role": "assistant", "content": assistant_msg})
     messages.append({"role": "user", "content": message})
-    # Not using stream
     response = client.chat_completion(
         messages,
         max_tokens=500,
         temperature=0.1
     )
     return response['choices'][0]['message']['content'].strip()
+
 def custom_chat_ui():
-    with gr.Blocks() as demo:
+    with gr.Blocks(
+        theme=gr.themes.Soft(
+            primary_hue="pink",
+            secondary_hue="orange",
+            neutral_hue="yellow",
+            spacing_size="lg",
+            radius_size="lg",  # ← FIXED: was "xl"
+            font=[gr.themes.GoogleFont("Quicksand"), "sans-serif"],
+            font_mono=[gr.themes.GoogleFont("IBM Plex Mono"), "monospace"]
+        )
+    ) as demo:
         gr.Image(
-            value="pitbull.jpg",
             show_label=False,
             show_share_button=False,
-            show_download_button=False
+            show_download_button=False,
+            container=False,
+            height=200
         )
-        gr.ChatInterface(respond)
+        gr.ChatInterface(respond, title="🌸 Cycle-Aware Wellness Coach 🌞")
     return demo
-custom_chat_ui().launch()
+
+custom_chat_ui().launch()