initial

app.py

@@ -1,64 +1,125 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import streamlit as st
+from transformers import AutoTokenizer, AutoModel
+from sklearn.decomposition import PCA
+import torch
+import numpy as np
+import plotly.graph_objects as go
+import math
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load transformer model + tokenizer..
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+    model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+    return tokenizer, model
 
+tokenizer, model = load_model()
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Encode using mean pooling
+def encode_texts(texts):
+    with torch.no_grad():
+        inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+        output = model(**inputs)
+        mask = inputs["attention_mask"].unsqueeze(-1).expand(output.last_hidden_state.shape).float()
+        pooled = torch.sum(output.last_hidden_state * mask, dim=1) / mask.sum(dim=1)
+        return pooled.cpu().numpy()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Session state init
+if "submitted_text" not in st.session_state:
+    st.session_state.submitted_text = """BMW
+Porsche
+Mercedes
+Coffee
+Tea
+Water
+Germany
+Italy
+Brazil
+Violin
+Drums
+Trumpet
+Man
+Women
+Child"""
 
-    messages.append({"role": "user", "content": message})
+# UI layout
+col1, col2 = st.columns([1, 3])
 
-    response = ""
+with col1:
+    st.title("🧠 Embedding Input")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    with st.form(key="embedding_input_form"):
+        st.form_submit_button("✅ Submit Text")
+        st.text_area(
+            label="Enter words (one per line)",
+            key="submitted_text",
+            height=400,
+        )
 
-        response += token
-        yield response
+texts = [t.strip() for t in st.session_state.submitted_text.split("\n") if t.strip()]
+if len(texts) < 3:
+    st.warning("Please enter at least three words.")
+    st.stop()
 
+embeddings = encode_texts(texts)
+coords = PCA(n_components=3).fit_transform(embeddings)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Rotation frames
+frames = []
+for angle in range(0, 360, 2):
+    rad = math.radians(angle)
+    camera = dict(eye=dict(x=2 * math.cos(rad), y=2 * math.sin(rad), z=0.7))
+    frames.append(go.Frame(layout=dict(scene_camera=camera)))
+
+# Plotly figure with animation controls
+fig = go.Figure(
+    data=[
+        go.Scatter3d(
+            x=coords[:, 0],
+            y=coords[:, 1],
+            z=coords[:, 2],
+            mode="markers+text",
+            text=texts,
+            textposition="top center",
+            textfont=dict(color="black"),
+            marker=dict(size=6),
+        )
     ],
+    layout=go.Layout(
+        title="3D Embedding Projection",
+        scene=dict(
+            xaxis=dict(title="X", showbackground=True, backgroundcolor="rgba(255,0,0,0.4)"),
+            yaxis=dict(title="Y", showbackground=True, backgroundcolor="rgba(0,255,0,0.4)"),
+            zaxis=dict(title="Z", showbackground=True, backgroundcolor="rgba(0,0,255,0.4)"),
+        ),
+        updatemenus=[
+            dict(
+                type="buttons",
+                showactive=False,
+                buttons=[
+                    dict(
+                        label="🔄 Rotate",
+                        method="animate",
+                        args=[
+                            None,
+                            dict(
+                                frame=dict(duration=50, redraw=True),
+                                transition=dict(duration=0),
+                                fromcurrent=True,
+                                mode="immediate"
+                            )
+                        ],
+                    )
+                ],
+                x=0.05,
+                y=0.9
+            )
+        ],
+        margin=dict(l=0, r=0, b=0, t=30),
+    ),
+    frames=frames
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+with col2:
+    st.title("📊 3D Plot")
+    st.plotly_chart(fig, use_container_width=True)