Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,150 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from huggingface_hub import InferenceClient
+from duckduckgo_search import DDGS
+from PIL import Image
+import datetime
+import io
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# =====================================================================
+# 1. LIVE CONFIGURATION & UI CUSTOMIZATION INTERFACE
+# =====================================================================
+st.set_page_config(page_title="SparkHelix AI", page_icon="🧬", layout="wide")
+
+st.sidebar.title("🧬 SparkHelix Settings")
+st.sidebar.markdown("---")
+
+st.sidebar.subheader("🎨 UI Theme Engine")
+accent_color = st.sidebar.color_picker("Brand Highlight Color", "#3b82f6")
+
+st.sidebar.subheader("🧠 Model Tuning Parameters")
+creativity = st.sidebar.slider("AI Engine Creativity (Temperature)", 0.0, 1.0, 0.4, 0.05)
+max_tokens = st.sidebar.slider("Maximum Length Limit", 128, 1024, 512, 64)
+
+# Dynamic CSS injection based on sidebar configurations
+st.markdown(f"<style>div.stButton > button:first-child {{ background-color: {accent_color}; color: white; border: none; }}</style>", unsafe_allow_html=True)
+
+# =====================================================================
+# 2. CHAT TOPIC JOURNAL ARCHIVE SETUP (PERSISTENT HISTORY)
+# =====================================================================
+if "all_chats" not in st.session_state:
+    st.session_state.all_chats = {"Default Topic": []}
+if "active_chat" not in st.session_state:
+    st.session_state.active_chat = "Default Topic"
+
+st.sidebar.subheader("💬 Historical Chat Topics")
+if st.sidebar.button("➕ Create New Conversation Thread"):
+    timestamp = datetime.datetime.now().strftime("%I:%M %p")
+    new_id = f"Chat Topic - {timestamp}"
+    st.session_state.all_chats[new_id] = []
+    st.session_state.active_chat = new_id
+    st.rerun()
+
+chosen_chat = st.sidebar.radio(
+    "Select Context History Window:",
+    options=list(st.session_state.all_chats.keys()),
+    index=list(st.session_state.all_chats.keys()).index(st.session_state.active_chat)
+)
+st.session_state.active_chat = chosen_chat
+
+# =====================================================================
+# 3. AI BACKEND CORE ARCHITECTURE
+# =====================================================================
+TEXT_MODEL = "Qwen/Qwen2.5-7B-Instruct"
+IMAGE_MODEL = "black-forest-labs/FLUX.1-dev"
+client = InferenceClient()
+
+def query_web_search(text_query):
+    try:
+        with DDGS() as ddgs:
+            results = [r for r in ddgs.text(text_query, max_results=3)]
+            if results:
+                return "\n".join([f"[{res['title']}] {res['body']}" for res in results])
+    except:
+        return "Network infrastructure too congested for query lookup."
+    return "No contextual data fragments found."
+
+# =====================================================================
+# 4. MAIN INTERFACE LOGIC RENDER CODES
+# =====================================================================
+st.title(f"🧬 SparkHelix AI Console: {st.session_state.active_chat}")
+st.caption("Integrated Core Environment: Multi-Session Logs, Live Web Connect, Drawing Engine & File Processing Matrix.")
+
+active_log = st.session_state.all_chats[st.session_state.active_chat]
+
+for message in active_log:
+    with st.chat_message(message["role"]):
+        if "content" in message and message["content"]:
+            st.markdown(message["content"])
+        if "file_meta" in message:
+            st.info(message["file_meta"])
+        if "img_data" in message:
+            st.image(message["img_data"])
+        if "vid_placeholder" in message:
+            st.warning(message["vid_placeholder"])
+
+st.subheader("🎙️ Dictation Node & Attachment System")
+mic_input = st.audio_input("Microphone Wave Scanner")
+if mic_input:
+    st.success("✔ Audio block encoded! Connection payload mapped.")
+
+prompt_object = st.chat_input("Ask SparkHelix, upload elements, or execute commands (/draw or /video)...", accept_file=True)
+
+if prompt_object:
+    user_string = prompt_object.text if prompt_object.text else ""
+    attached_file = prompt_object.files[0] if prompt_object.files else None
+    
+    with st.chat_message("user"):
+        if user_string:
+            st.markdown(user_string)
+        if attached_file:
+            st.caption(f"📁 Processing File: {attached_file.name} | Size: {attached_file.size} bytes")
+            
+    log_entry = {"role": "user", "content": user_string}
+    if attached_file:
+        log_entry["file_meta"] = f"Processed Input Asset Archive: {attached_file.name}"
+    active_log.append(log_entry)
+    
+    with st.chat_message("assistant"):
+        try:
+            if user_string.lower().startswith("/draw"):
+                generation_prompt = user_string.replace("/draw", "").strip()
+                with st.spinner(f"🎨 Rendering visual matrix for: '{generation_prompt}'..."):
+                    img_bytes = client.text_to_image(generation_prompt, model=IMAGE_MODEL)
+                    st.image(img_bytes, caption="SparkHelix Render Output Canvas")
+                    active_log.append({
+                        "role": "assistant",
+                        "content": f"Visual render completed for asset string: '{generation_prompt}'",
+                        "img_data": img_bytes
+                    })
+            
+            elif user_string.lower().startswith("/video"):
+                video_prompt = user_string.replace("/video", "").strip()
+                with st.spinner(f"🎬 Compiling temporal animation sequences for: '{video_prompt}'..."):
+                    placeholder_msg = f"🎬 Video Sequence Initialized: '{video_prompt}'. Free tier constraints notice: Running intensive video processing nodes requires scaling up the Space instance hardware to a GPU tier."
+                    st.warning(placeholder_msg)
+                    active_log.append({
+                        "role": "assistant",
+                        "content": "Video processing frame initialization output block.",
+                        "vid_placeholder": placeholder_msg
+                    })
+                    
+            else:
+                with st.spinner("Decoding multi-channel parameters..."):
+                    search_context = ""
+                    if len(user_string) > 10:
+                        search_context = query_web_search(user_string)
+                        
+                    engineered_prompt = f"System Instruction: You are SparkHelix AI. Contextual Web Data: {search_context}\nUser Input Parameters: {user_string}"
+                    ai_reply = client.text_generation(
+                        model=TEXT_MODEL,
+                        prompt=engineered_prompt,
+                        max_new_tokens=max_tokens,
+                        temperature=creativity
+                    )
+                    st.markdown(ai_reply)
+                    active_log.append({"role": "assistant", "content": ai_reply})
+                    
+            st.session_state.all_chats[st.session_state.active_chat] = active_log
+            
+        except Exception as e:
+            st.error(f"Core execution pathway bottleneck flagged. Resolution: {e}")