Add Product MVP and Video Brain modes + Render deployment

Dockerfile.render

@@ -0,0 +1,33 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    supervisor \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+# Copy and install Python dependencies (cached layer)
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy ALL application code
+COPY . .
+
+# Create supervisor config directory
+RUN mkdir -p /etc/supervisor/conf.d
+
+# Create workspace directories
+RUN mkdir -p /app/workspace_data /app/chroma_db
+
+# Expose both ports
+EXPOSE 8000 8501
+
+# Copy supervisor config
+COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf
+
+# Run supervisor to manage both processes
+CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"]

app/api.py

@@ -992,3 +992,243 @@ def agentic_mode(req: ModeRequest):
     )
 
 
+# =======================================================
+# PRODUCT MVP ENDPOINT - Generates MVP Blueprints
+# =======================================================
+class ProductMVPRequest(BaseModel):
+    message: str
+    workspace_id: str = "default"
+    mode: str = "product_mvp"
+
+
+@app.post("/api/product_mvp", response_model=ChatResponse)
+def product_mvp_mode(req: ProductMVPRequest):
+    """
+    Product MVP Mode - Generates comprehensive MVP blueprints from product ideas.
+    Includes product name, pitch, target users, features, architecture, tech stack, and more.
+    """
+    q = req.message.strip()
+    ws = req.workspace_id
+    
+    memory.add(ws, "user", q)
+    print(f"\n🚀 PRODUCT MVP MODE: {q}")
+    
+    # Research similar products and market
+    market_research = ""
+    try:
+        results = search_tool.search(f"{q} startup MVP product", num_results=3)
+        if results:
+            for r in results:
+                url = r.get("url", "")
+                text = browse_tool.fetch_clean(url)
+                if text:
+                    market_research += text[:800] + "\n\n"
+    except Exception as e:
+        print(f"Market research error: {e}")
+    
+    prompt = f"""You are a PRODUCT BUILDER AI that creates comprehensive MVP blueprints.
+
+The user wants to build: {q}
+
+{f"MARKET RESEARCH (use for context):{chr(10)}{market_research}" if market_research else ""}
+
+Generate a COMPLETE MVP Blueprint with the following sections. Use markdown formatting with tables where appropriate:
+
+# 📄 MVP Blueprint – [Product Name]
+A one-line description of the product.
+
+## 1. Product Name
+Create a catchy, memorable product name.
+
+## 2. One‑Line Pitch
+A compelling pitch in quotes that explains the value proposition.
+
+## 3. Target Users
+Create a markdown table with columns: Persona | Age | Occupation | Goals | Pain Points | How [Product] Helps
+Include 4-5 different user personas.
+
+## 4. Problems to Solve
+List 5 key problems the product solves with bullet points.
+
+## 5. MVP Features
+Create a table with: Feature | Description | Priority (Must-have/Nice-to-have)
+Include 8-10 features.
+
+## 6. User Journey (Step‑by‑Step)
+Number each step of the user journey from landing to retention.
+
+## 7. System Architecture
+Create an ASCII diagram showing the system components and their connections.
+Include: Frontend, Backend, Database, APIs, Third-party services.
+
+## 8. Database Tables
+Create a table showing the main database tables with: Table | Columns | Notes
+
+## 9. API Endpoints (REST)
+Create a table with: Method | Endpoint | Description | Auth Required
+
+## 10. Tech Stack
+Create a table with: Layer | Technology | Reason
+Cover: Frontend, Backend, Auth, Database, Cache, Storage, Hosting, CI/CD, Monitoring
+
+## 11. Future Features (Post‑MVP)
+List 8 features for after MVP launch.
+
+## Next Steps
+List 5 actionable next steps to start building.
+
+End with: **Happy building! 🚀**
+
+Be detailed, practical, and use real-world best practices. Make it production-ready."""
+
+    msgs = build_context(ws, prompt)
+    answer = llm.invoke(msgs).content
+    
+    # Generate follow-up questions
+    follow = [
+        "Generate wireframes for core screens",
+        "Create a development timeline",
+        "Estimate the MVP budget",
+        "Design the database schema in detail",
+        "Write user stories for MVP features"
+    ]
+    
+    memory.add(ws, "assistant", answer)
+    print(f"  ✅ Product MVP: Blueprint generated")
+    
+    return ChatResponse(
+        answer=answer,
+        sources=[],
+        links=[],
+        images=[],
+        followups=follow,
+        default_tab="answer",
+        workspace_id=ws
+    )
+
+
+# =======================================================
+# VIDEO BRAIN ENDPOINT - YouTube Video Analysis
+# =======================================================
+class VideoBrainRequest(BaseModel):
+    message: str
+    workspace_id: str = "default"
+    mode: str = "video_brain"
+    youtube_url: str = ""
+
+
+@app.post("/api/video_brain", response_model=ChatResponse)
+def video_brain_mode(req: VideoBrainRequest):
+    """
+    Video Brain Mode - Analyzes YouTube videos and answers questions about them.
+    Extracts transcript/content and provides intelligent responses.
+    """
+    q = req.message.strip()
+    ws = req.workspace_id
+    youtube_url = req.youtube_url
+    
+    memory.add(ws, "user", q)
+    print(f"\n🎥 VIDEO BRAIN MODE: {q}")
+    print(f"  📺 YouTube URL: {youtube_url}")
+    
+    if not youtube_url:
+        return ChatResponse(
+            answer="⚠️ Please provide a YouTube URL first. Enter the URL in the Video Brain interface and click 'Load' before asking questions.",
+            sources=[],
+            links=[],
+            images=[],
+            followups=[],
+            default_tab="answer",
+            workspace_id=ws
+        )
+    
+    # Try to get video information
+    video_content = ""
+    video_title = ""
+    
+    try:
+        # Extract video ID
+        video_id = ""
+        if "v=" in youtube_url:
+            video_id = youtube_url.split("v=")[1].split("&")[0]
+        elif "youtu.be/" in youtube_url:
+            video_id = youtube_url.split("youtu.be/")[1].split("?")[0]
+        
+        print(f"  🔍 Video ID: {video_id}")
+        
+        # Search for video information and related content
+        if video_id:
+            # Search for the video title and description
+            topic_results = search_tool.search(f"youtube {video_id}", num_results=3)
+            if topic_results:
+                for r in topic_results:
+                    title = r.get("title", "")
+                    if title and not video_title:
+                        video_title = title
+                    snippet = r.get("content", "") or r.get("snippet", "")
+                    if snippet:
+                        video_content += snippet + "\n"
+        
+        # Search for transcript or summary
+        search_query = f"youtube video transcript summary {video_title or video_id}"
+        results = search_tool.search(search_query, num_results=3)
+        
+        for r in results[:2]:
+            url = r.get("url", "")
+            if url and "youtube.com" not in url:  # Skip YouTube pages, get transcripts
+                text = browse_tool.fetch_clean(url)
+                if text:
+                    video_content += text[:2000] + "\n\n"
+        
+        print(f"  📝 Content gathered: {len(video_content)} chars")
+        
+    except Exception as e:
+        print(f"  ❌ Video content fetch error: {e}")
+    
+    prompt = f"""You are VIDEO BRAIN AI - an expert at analyzing and explaining YouTube video content.
+
+VIDEO URL: {youtube_url}
+{f"VIDEO TITLE: {video_title}" if video_title else ""}
+
+{f"AVAILABLE VIDEO CONTEXT:{chr(10)}{video_content[:4000]}" if video_content else "Note: Could not fetch video transcript directly. I will provide helpful guidance based on the question and general knowledge."}
+
+USER QUESTION: {q}
+
+Instructions:
+1. If context is available, answer based on the video content
+2. If the question is about summarizing, provide key points and takeaways
+3. If asking about specific topics, explain them clearly
+4. Use timestamps if available (e.g., "At around 5:30...")
+5. If limited information is available, be honest but still provide helpful guidance
+6. Format your response with headers and bullet points for clarity
+7. Make the response educational and easy to understand
+
+Provide a comprehensive, helpful response:"""
+
+    msgs = build_context(ws, prompt)
+    answer = llm.invoke(msgs).content
+    
+    # Generate follow-up questions about the video
+    follow = [
+        "Summarize the main points of this video",
+        "What are the key takeaways?",
+        "Explain the most important concept covered",
+        "What questions should I ask about this topic?",
+        "Create study notes from this video"
+    ]
+    
+    sources = [{"title": f"🎥 {video_title or 'YouTube Video'}", "url": youtube_url}]
+    links = [{"title": video_title or "YouTube Video", "url": youtube_url, "snippet": "Source video"}]
+    
+    memory.add(ws, "assistant", answer)
+    print(f"  ✅ Video Brain: Response generated")
+    
+    return ChatResponse(
+        answer=answer,
+        sources=sources,
+        links=links,
+        images=[],
+        followups=follow,
+        default_tab="answer",
+        workspace_id=ws
+    )

config/config.py

@@ -8,7 +8,7 @@ class Config:
     GROQ_API_KEY = os.getenv("GROQ_API_KEY")
     TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 
-    LLM_MODEL = "llama-3.1-70b-versatile"
+    LLM_MODEL = "openai/gpt-oss-120b"
 
     CHUNK_SIZE = 400
     CHUNK_OVERLAP = 80

rag/graph_deep.py

@@ -110,13 +110,13 @@ class WebSearchGraph:
         
         g.add_node("search", self.search_node.search)
         g.add_node("fetch", self.fetch_node.fetch)
-        g.add_node("context", self.context_node.build_context)
+        g.add_node("build_context", self.context_node.build_context)
         g.add_node("answer", self.answer_node.answer)
         
         g.set_entry_point("search")
         g.add_edge("search", "fetch")
-        g.add_edge("fetch", "context")
-        g.add_edge("context", "answer")
+        g.add_edge("fetch", "build_context")
+        g.add_edge("build_context", "answer")
         g.add_edge("answer", END)
         
         self.graph = g.compile()
@@ -148,12 +148,12 @@ class RAGOnlyGraph:
         g = StateGraph(RAGOnlyState)
         
         g.add_node("retrieve", self.retrieve_node.retrieve)
-        g.add_node("context", self.context_node.build_context)
+        g.add_node("build_context", self.context_node.build_context)
         g.add_node("answer", self.answer_node.answer)
         
         g.set_entry_point("retrieve")
-        g.add_edge("retrieve", "context")
-        g.add_edge("context", "answer")
+        g.add_edge("retrieve", "build_context")
+        g.add_edge("build_context", "answer")
         g.add_edge("answer", END)
         
         self.graph = g.compile()

render.yaml

@@ -0,0 +1,20 @@
+services:
+  - type: web
+    name: perplexity-clone
+    env: docker
+    dockerfilePath: ./Dockerfile.render
+    dockerContext: .
+    plan: free
+    region: oregon
+    healthCheckPath: /health
+    envVars:
+      - key: ANTHROPIC_API_KEY
+        sync: false
+      - key: TAVILY_API_KEY
+        sync: false
+      - key: GOOGLE_API_KEY
+        sync: false
+      - key: GOOGLE_CSE_ID
+        sync: false
+      - key: BACKEND_URL
+        value: http://localhost:8000

streamlit_app.py

@@ -28,6 +28,12 @@ if "uploaded_files" not in st.session_state:
     st.session_state.uploaded_files = []
 if "show_upload" not in st.session_state:
     st.session_state.show_upload = False
+if "youtube_url" not in st.session_state:
+    st.session_state.youtube_url = ""
+if "video_loaded" not in st.session_state:
+    st.session_state.video_loaded = False
+if "product_ideas" not in st.session_state:
+    st.session_state.product_ideas = []
 
 # =====================================
 # CONFIGURATION
@@ -78,6 +84,16 @@ MODES = {
         "desc": "Direct AI chat",
         "endpoint": "/api/focus"
     },
+    "Product MVP": {
+        "icon": "🚀",
+        "desc": "Idea → MVP Blueprint",
+        "endpoint": "/api/product_mvp"
+    },
+    "Video Brain": {
+        "icon": "🎥",
+        "desc": "Understand YouTube lectures",
+        "endpoint": "/api/video_brain"
+    },
 }
 
 # =====================================
@@ -443,7 +459,7 @@ st.markdown(get_css(), unsafe_allow_html=True)
 # =====================================
 # HELPER FUNCTIONS
 # =====================================
-def call_api(query: str, mode: str):
+def call_api(query: str, mode: str, extra_data: dict = None):
     """Call backend API based on selected mode."""
     mode_config = MODES.get(mode, MODES["Automatic"])
     endpoint = mode_config["endpoint"]
@@ -454,9 +470,23 @@ def call_api(query: str, mode: str):
         "mode": mode.lower().replace(" ", "_")
     }
     
+    # Add extra data for special modes
+    if extra_data:
+        payload.update(extra_data)
+    
     try:
         response = requests.post(f"{API_URL}{endpoint}", json=payload, timeout=180)
-        return response.json()
+        response.raise_for_status()
+        try:
+            return response.json()
+        except ValueError:
+            return {
+                "answer": f"Error: Invalid JSON response from server",
+                "sources": [],
+                "links": [],
+                "images": [],
+                "followups": []
+            }
     except Exception as e:
         return {
             "answer": f"Error: {str(e)}",
@@ -574,6 +604,56 @@ st.markdown('</div>', unsafe_allow_html=True)
 # Mode description
 st.markdown(f'<div class="mode-desc">{MODES[st.session_state.mode]["icon"]} {st.session_state.mode}: {MODES[st.session_state.mode]["desc"]}</div>', unsafe_allow_html=True)
 
+# =====================================
+# SPECIAL UI FOR PRODUCT MVP MODE
+# =====================================
+if st.session_state.mode == "Product MVP" and not st.session_state.current_result:
+    st.markdown("""
+    <div style="text-align: center; padding: 20px; margin: 20px auto; max-width: 700px; 
+                background: linear-gradient(135deg, #FF6B35 0%, #F7931E 100%); 
+                border-radius: 16px; color: white;">
+        <h3 style="margin: 0; font-size: 24px;">🚀 Product Builder – Idea → MVP Blueprint</h3>
+        <p style="margin: 10px 0 0; opacity: 0.9;">🟠 Product Builder Active</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.markdown("<p style='text-align: center; color: #888; margin: 15px 0;'>Describe your product idea:</p>", unsafe_allow_html=True)
+
+# =====================================
+# SPECIAL UI FOR VIDEO BRAIN MODE
+# =====================================
+if st.session_state.mode == "Video Brain" and not st.session_state.current_result:
+    st.markdown("""
+    <div style="text-align: center; padding: 20px; margin: 20px auto; max-width: 700px; 
+                background: linear-gradient(135deg, #3B82F6 0%, #8B5CF6 100%); 
+                border-radius: 16px; color: white;">
+        <h3 style="margin: 0; font-size: 24px;">🎥 Video Brain – Understand Any YouTube Lecture</h3>
+        <p style="margin: 10px 0 0; opacity: 0.9;">🔵 Upload Video First</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # YouTube URL input
+    col_yt1, col_yt2 = st.columns([5, 1])
+    with col_yt1:
+        youtube_url = st.text_input(
+            "youtube_url",
+            placeholder="Enter YouTube URL (e.g., https://youtube.com/watch?v=...)",
+            label_visibility="collapsed",
+            key="youtube_url_input"
+        )
+    with col_yt2:
+        if st.button("📺 Load", key="load_video_btn"):
+            if youtube_url and ("youtube.com" in youtube_url or "youtu.be" in youtube_url):
+                st.session_state.youtube_url = youtube_url
+                st.session_state.video_loaded = True
+                st.success("✅ Video loaded! Now ask questions about it.")
+            else:
+                st.error("Please enter a valid YouTube URL")
+    
+    if st.session_state.video_loaded and st.session_state.youtube_url:
+        st.success(f"📺 Video ready: {st.session_state.youtube_url[:50]}...")
+        st.markdown("<p style='text-align: center; color: #888; margin: 15px 0;'>Ask about the video:</p>", unsafe_allow_html=True)
+
 # Show file uploader when icon is clicked
 if st.session_state.show_upload:
     uploaded = st.file_uploader(
@@ -602,8 +682,25 @@ if st.session_state.uploaded_files:
 # HANDLE SEARCH
 # =====================================
 if submit and query.strip():
+    extra_data = None
+    
+    # For Video Brain mode, include the YouTube URL
+    if st.session_state.mode == "Video Brain":
+        if st.session_state.video_loaded and st.session_state.youtube_url:
+            extra_data = {"youtube_url": st.session_state.youtube_url}
+        else:
+            st.warning("⚠️ Please load a YouTube video first!")
+            st.stop()
+    
+    # For Product MVP mode, save to ideas history
+    if st.session_state.mode == "Product MVP":
+        st.session_state.product_ideas.append({
+            "idea": query.strip(),
+            "time": "just now"
+        })
+    
     with st.spinner(f"🔄 {st.session_state.mode}..."):
-        result = call_api(query.strip(), st.session_state.mode)
+        result = call_api(query.strip(), st.session_state.mode, extra_data)
         st.session_state.current_result = {
             "query": query.strip(),
             "mode": st.session_state.mode,
@@ -621,6 +718,26 @@ if st.session_state.current_result:
     
     st.divider()
     
+    # Special header for Product MVP mode
+    if result['mode'] == "Product MVP":
+        st.markdown("""
+        <div style="text-align: center; padding: 15px; margin: 10px auto; max-width: 700px; 
+                    background: linear-gradient(135deg, #FF6B35 0%, #F7931E 100%); 
+                    border-radius: 12px; color: white;">
+            <h4 style="margin: 0;">📄 MVP Blueprint</h4>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Special header for Video Brain mode
+    if result['mode'] == "Video Brain":
+        st.markdown("""
+        <div style="text-align: center; padding: 15px; margin: 10px auto; max-width: 700px; 
+                    background: linear-gradient(135deg, #3B82F6 0%, #8B5CF6 100%); 
+                    border-radius: 12px; color: white;">
+            <h4 style="margin: 0;">🎥 Video Analysis</h4>
+        </div>
+        """, unsafe_allow_html=True)
+    
     # Query box
     mode_info = MODES.get(result['mode'], MODES['Automatic'])
     st.markdown(f"""
@@ -635,6 +752,10 @@ if st.session_state.current_result:
     if sources:
         st.success(f"✓ {len(sources)} sources")
     
+    # Memory saved notification for Product MVP
+    if result['mode'] == "Product MVP":
+        st.info("📝 New Memory Saved")
+    
     # Layout - Full width (removed duplicate sidebar sources)
     tabs = st.tabs(["✨ Answer", "🔗 Sources", "🖼️ Images"])
     
@@ -649,8 +770,11 @@ if st.session_state.current_result:
             st.markdown("**Related:**")
             for i, fu in enumerate(followups[:3]):
                 if st.button(f"→ {fu}", key=f"fu_{i}"):
+                    extra = None
+                    if st.session_state.mode == "Video Brain" and st.session_state.youtube_url:
+                        extra = {"youtube_url": st.session_state.youtube_url}
                     with st.spinner("..."):
-                        new_result = call_api(fu, st.session_state.mode)
+                        new_result = call_api(fu, st.session_state.mode, extra)
                         st.session_state.current_result = {
                             "query": fu,
                             "mode": st.session_state.mode,
@@ -700,6 +824,11 @@ with st.sidebar:
         st.session_state.uploaded_files = []
         st.info("Files cleared")
     
+    if st.button("🗑️ Clear Video", use_container_width=True):
+        st.session_state.youtube_url = ""
+        st.session_state.video_loaded = False
+        st.info("Video cleared")
+    
     st.divider()
     st.caption(f"Theme: {'🌙 Dark' if st.session_state.theme == 'dark' else '☀️ Light'}")
     st.caption(f"Mode: {st.session_state.mode}")
@@ -709,3 +838,16 @@ with st.sidebar:
         st.markdown("### 📁 Files")
         for f in st.session_state.uploaded_files:
             st.caption(f"📄 {f}")
+    
+    # Show video info for Video Brain mode
+    if st.session_state.video_loaded and st.session_state.youtube_url:
+        st.divider()
+        st.markdown("### 🎥 Loaded Video")
+        st.caption(f"📺 {st.session_state.youtube_url[:40]}...")
+    
+    # Show recent product ideas
+    if st.session_state.product_ideas:
+        st.divider()
+        st.markdown("### 🧾 Recent Ideas")
+        for idea in st.session_state.product_ideas[-3:]:
+            st.caption(f"💡 {idea['idea'][:30]}...")

supervisord.conf

@@ -0,0 +1,29 @@
+[supervisord]
+nodaemon=true
+user=root
+logfile=/dev/stdout
+logfile_maxbytes=0
+loglevel=info
+
+[program:fastapi]
+command=uvicorn app.api:app --host 0.0.0.0 --port 8000 --workers 1
+directory=/app
+autostart=true
+autorestart=true
+stdout_logfile=/dev/stdout
+stdout_logfile_maxbytes=0
+stderr_logfile=/dev/stderr
+stderr_logfile_maxbytes=0
+priority=1
+
+[program:streamlit]
+command=streamlit run streamlit_app.py --server.port=8501 --server.address=0.0.0.0 --server.headless=true --browser.gatherUsageStats=false
+directory=/app
+autostart=true
+autorestart=true
+stdout_logfile=/dev/stdout
+stdout_logfile_maxbytes=0
+stderr_logfile=/dev/stderr
+stderr_logfile_maxbytes=0
+priority=2
+startsecs=10

tools/knowledge_panel.py

@@ -25,6 +25,7 @@ class KnowledgePanel:
         try:
             url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{query.replace(' ', '_')}"
             r = requests.get(url, timeout=10)
+            r.raise_for_status()
             data = r.json()
 
             return {
@@ -34,7 +35,8 @@ class KnowledgePanel:
                 "thumbnail": data.get("thumbnail", {}).get("source", ""),
                 "url": data.get("content_urls", {}).get("desktop", {}).get("page", "")
             }
-        except:
+        except (requests.exceptions.RequestException, ValueError) as e:
+            print(f"Wikipedia API error: {e}")
             return {}
 
     def get_fast_facts(self, query: str) -> List[str]:

tools/search_tool.py

@@ -16,7 +16,11 @@ class SearchTool:
         url = "https://api.tavily.com/search"
         payload = {"query": query, "num_results": num_results}
         headers = {"Authorization": self.api_key}
-        resp = requests.post(url, json=payload, headers=headers, timeout=20)
-        resp.raise_for_status()
-        data = resp.json()
-        return data.get("results", [])
+        try:
+            resp = requests.post(url, json=payload, headers=headers, timeout=20)
+            resp.raise_for_status()
+            data = resp.json()
+            return data.get("results", [])
+        except (requests.exceptions.RequestException, ValueError) as e:
+            print(f"Search error: {e}")
+            return []