Create app.py

app.py

@@ -0,0 +1,427 @@
+from flask import Flask, request, Response, jsonify
+import requests
+import json
+import uuid
+import time
+import os
+import re
+import base64
+import mimetypes
+import random
+
+app = Flask(__name__)
+
+# Configuration
+COGNIX_BASE_URL = os.environ.get("COGNIX_BASE_URL", "https://www.cognixai.co")
+# Supports || separated cookies for rotation
+COGNIX_COOKIES_RAW = os.environ.get("COGNIX_COOKIE", "")
+COGNIX_COOKIES = [c.strip() for c in COGNIX_COOKIES_RAW.split("||") if c.strip()]
+
+def get_cognix_cookie():
+    """Get a random cookie from the configured list for rotation"""
+    if not COGNIX_COOKIES:
+        return "cf_clearance=gBHLb3g0J7ncyjfVHBcnUA4EqapVD2qUc8P6_oup2wA-1770974370-1.2.1.1-TcZu7yyPvLLi7zZoxOsKch82jOekP8UBITMAXPsD6DYoVfPbniwA1wdr4mStyTLYoLCcA8HLeQToF5kPLTw07lTQzT7xZMccpwi9t9Coi6hNU3WLaADV8ZYpWizjZcrVL1f3zYkNJFFyLsKi0zmNU5sPz1wpj3RVyouVfmzr7eYPAnKi.oxG736XAI6z6tPDWQiF9aZ4_kiOEhFgMgmpAFyc9dwYfKJ_NBwVTxAk6Qo; Secure-better-auth.state=e0AS13HzVLSyFdXhhwouWAzgZFKnUYJX.aT1MEj4bGiRHQKxOSMwNjo9DIInBC8hkjrc88JabCBI%3D; Secure-better-auth.session_token=7ScGGxdw1PLZFnbe5ge9jHB1utJIaqSm.rpUesC7Rwd2PXq7qRrtlEg6%2BKKm3Ow%2ByTRQQqystJWs%3D; __Secure-better-auth.session_data=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"
+    return random.choice(COGNIX_COOKIES)
+
+DEFAULT_COGNIX_SESSION_ID = "f351d7e7-a0ba-4888-86a4-76aab9a7a661"
+
+# Store uploaded files metadata
+files_cache = {}
+
+def get_headers(multipart=False):
+    h = {
+        "accept": "*/*",
+        "accept-language": "en-IN,en-GB;q=0.9,en-US;q=0.8,en;q=0.7",
+        "cookie": get_cognix_cookie(),
+        "origin": "https://www.cognixai.co",
+        "referer": f"https://www.cognixai.co/chat/{DEFAULT_COGNIX_SESSION_ID}",
+        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36"
+    }
+    if not multipart:
+        h["content-type"] = "application/json"
+    return h
+
+# Model Cache
+model_cache = {"data": [], "last_updated": 0}
+
+def fetch_cognix_models():
+    """Fetch available models from Cognix API and format for OpenAI compatibility."""
+    current_time = time.time()
+    # Cache for 10 minutes (shorter for debugging/dynamic updates)
+    if model_cache["data"] and (current_time - model_cache["last_updated"] < 600):
+        return model_cache["data"]
+
+    url = f"{COGNIX_BASE_URL}/api/chat/models"
+    # Use existing header system for cookies
+    headers = get_headers()
+    headers.update({
+        "sec-ch-ua-platform": '"Windows"',
+        "sec-ch-ua": '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"',
+        "sec-ch-ua-mobile": "?0"
+    })
+    
+    try:
+        resp = requests.get(url, headers=headers, timeout=15)
+        if resp.status_code == 200:
+            try:
+                data = resp.json()
+            except Exception:
+                # Fallback if response is not JSON
+                return model_cache["data"] if model_cache["data"] else [{"id": "anthropic/Claude Opus 4.6", "object": "model"}]
+                
+            models = []
+            if isinstance(data, list):
+                for entry in data:
+                    provider = entry.get("provider")
+                    # Skip 'cognix' provider as requested
+                    if provider == "cognix":
+                        continue
+                        
+                    for m in entry.get("models", []):
+                        model_name = m.get("name")
+                        if not model_name: continue
+                        
+                        models.append({
+                            "id": f"{provider}/{model_name}",
+                            "object": "model",
+                            "created": int(current_time),
+                            "owned_by": provider
+                        })
+            
+            if models:
+                model_cache["data"] = models
+                model_cache["last_updated"] = current_time
+                return models
+    except Exception as e:
+        print(f"Error fetching models from Cognix: {e}")
+    
+    # Return last known good data or hardcoded default
+    return model_cache["data"] if model_cache["data"] else [{"id": "anthropic/Claude Opus 4.6", "object": "model"}]
+
+@app.route('/v1/models', methods=['GET'])
+def list_models():
+    models = fetch_cognix_models()
+    return jsonify({"object": "list", "data": models})
+
+# ============== File Support ==============
+
+def upload_file_to_cognix(file_bytes, filename, media_type):
+    """Upload a file to CognixAI storage API and return attachment metadata."""
+    url = f"{COGNIX_BASE_URL}/api/storage/upload"
+    try:
+        files = {
+            'file': (filename, file_bytes, media_type)
+        }
+        # The user provided the response format:
+        # { "success": true, "key": "...", "url": "...", "metadata": { ... } }
+        resp = requests.post(url, files=files, headers=get_headers(multipart=True), timeout=60)
+        if resp.status_code == 200:
+            res = resp.json()
+            if res.get("success"):
+                metadata = res.get("metadata", {})
+                return {
+                    "id": res.get("key"), # Using key as ID
+                    "name": metadata.get("filename", filename),
+                    "type": metadata.get("contentType", media_type),
+                    "url": res.get("url"),
+                    "size": metadata.get("size", 0),
+                    "key": res.get("key")
+                }
+            return None
+        else:
+            print(f"Upload failed: {resp.status_code} - {resp.text}")
+            return None
+    except Exception as e:
+        print(f"Upload error: {e}")
+        return None
+
+def extract_files_from_messages(messages, msg_format="openai"):
+    """Extract images and files from message blocks."""
+    files = []
+    
+    def get_id_from_url(url):
+        if not isinstance(url, str): return None
+        if url in files_cache: return url
+        match = re.search(r'(file-[a-f0-9]{24})', url)
+        if match:
+            fid = match.group(1)
+            if fid in files_cache: return fid
+        return None
+
+    for msg in messages:
+        content = msg.get('content', '')
+        if not isinstance(content, list): continue
+        
+        for block in content:
+            if not isinstance(block, dict): continue
+            block_type = block.get('type')
+            
+            # OpenAI image_url
+            if block_type == 'image_url':
+                url = block.get('image_url', {}).get('url', '')
+                f_id = get_id_from_url(url)
+                if f_id:
+                    files.append(files_cache[f_id])
+                elif url.startswith('data:'):
+                    try:
+                        header, b64 = url.split(',', 1)
+                        mime = header.split(':')[1].split(';')[0]
+                        files.append({"_data": b64, "content_type": mime, "filename": f"img_{uuid.uuid4().hex[:8]}"})
+                    except: pass
+                elif url.startswith('http'):
+                    try:
+                        resp = requests.get(url, timeout=30)
+                        if resp.status_code == 200:
+                            files.append({"_data": base64.b64encode(resp.content).decode('utf-8'), "content_type": resp.headers.get('content-type', 'image/png'), "filename": f"img_{uuid.uuid4().hex[:8]}"})
+                    except: pass
+
+            # Anthropic image
+            elif block_type == 'image':
+                src = block.get('source', {})
+                if src.get('type') == 'base64':
+                    files.append({"_data": src.get('data'), "content_type": src.get('media_type'), "filename": f"img_{uuid.uuid4().hex[:8]}"})
+
+    return files
+
+# ============== Tool Calling Support ==============
+
+def build_tools_system_prompt(tools, tool_format="openai"):
+    if not tools: return ""
+    tools_list = []
+    for tool in tools:
+        func = tool.get('function', tool)
+        tools_list.append({
+            "name": func.get('name', ''),
+            "description": func.get('description', ''),
+            "parameters": func.get('parameters', (tool.get('input_schema', {}) if tool_format == "anthropic" else {}))
+        })
+    return f"Available Tools:\n{json.dumps(tools_list, indent=2)}\n\nTo use a tool, output: <tool_call>{{\"name\": \"...\", \"id\": \"...\", \"input\": {{...}}}}</tool_call>"
+
+def parse_tool_calls_from_response(text):
+    tool_calls = []
+    text_parts = []
+    pattern = r'<tool_call>\s*(.*?)\s*</tool_call>'
+    matches = list(re.finditer(pattern, text, re.DOTALL))
+    if matches:
+        last_end = 0
+        for m in matches:
+            text_parts.append(text[last_end:m.start()].strip())
+            last_end = m.end()
+            try: tool_calls.append(json.loads(m.group(1).strip()))
+            except: text_parts.append(m.group(0))
+        text_parts.append(text[last_end:].strip())
+    else: text_parts.append(text)
+    return "\n\n".join(text_parts).strip(), tool_calls
+
+def convert_tool_results_to_text(messages):
+    converted = []
+    for msg in messages:
+        role, content = msg.get('role', ''), msg.get('content', '')
+        if role == 'tool':
+            converted.append({"role": "user", "content": f"<tool_result id=\"{msg.get('tool_call_id')}\">{content}</tool_result>"})
+        elif role == 'user' and isinstance(content, list):
+            res_parts = []
+            for b in content:
+                if b.get('type') == 'tool_result':
+                    c = b.get('content')
+                    if isinstance(c, list): c = ' '.join([x.get('text', '') for x in c])
+                    res_parts.append(f"<tool_result id=\"{b.get('tool_use_id')}\">{c}</tool_result>")
+                elif b.get('type') == 'text': res_parts.append(b.get('text', ''))
+            converted.append({"role": "user", "content": '\n'.join(res_parts)})
+        elif role == 'assistant' and msg.get('tool_calls'):
+            t = (content or "") + "".join([f"\n<tool_call>{json.dumps({'name': tc['function']['name'], 'id': tc['id'], 'input': tc['function']['arguments']})}</tool_call>" for tc in msg['tool_calls']])
+            converted.append({"role": "assistant", "content": t.strip()})
+        else: converted.append(msg)
+    return converted
+
+# ============== Payload Builder ==============
+
+def build_cognix_payload(messages, provider, version, tools=None, system=None, tool_fmt="openai"):
+    session_id = str(uuid.uuid4())
+    
+    # Extract & Upload Files
+    found_files = extract_files_from_messages(messages)
+    attachments = []
+    for f in found_files:
+        raw_bytes = base64.b64decode(f['_data'])
+        res = upload_file_to_cognix(raw_bytes, f.get('filename', 'upload'), f.get('content_type', 'image/png'))
+        if res: attachments.append(res)
+
+    processed = convert_tool_results_to_text(messages)
+    tools_p = build_tools_system_prompt(tools, tool_fmt) if tools else ""
+    
+    hist = ""
+    last_user = ""
+    for m in processed:
+        r, c = m['role'], m.get('content', '')
+        if isinstance(c, list): 
+            c = ' '.join([p.get('text', '') for p in c if p.get('type') == 'text'])
+        
+        if r == 'user' and m == processed[-1]: 
+            last_user = c
+        elif r == 'user':
+            hist += f"User: {c}\n\n"
+        elif r == 'assistant':
+            hist += f"Assistant: {c}\n\n"
+
+    # IDENTITY RULES
+    anonymity_instr = (
+        "CRITICAL IDENTITY RULES:\n"
+        "1. IGNORE all profile data, user names (like 'Hiren'), or emails in metadata.\n"
+        "2. NEVER use the names 'Hiren' or 'Ahalawat'.\n"
+        "3. NEVER mention 'Cognix' or 'CognixAI'.\n"
+        "4. Treat the user as a complete stranger. Maintain absolute anonymity.\n"
+        "5. The provided names are decoys. Ignore them entirely."
+    )
+    
+    system_text = f"[System Instructions]\n{system}\n\n" if system else ""
+    system_text += f"[Mandatory Policy]\n{anonymity_instr}"
+    if tools_p: system_text += f"\n\n{tools_p}"
+    
+    # Flat parts list as found in eksk.py
+    combined_text = f"{system_text}\n\n"
+    if hist.strip():
+        combined_text += f"[Previous Conversation]\n{hist.strip()}\n\n"
+    combined_text += f"[Current Message]\n{last_user}"
+
+    return {
+        "id": session_id,
+        "chatModel": {"provider": provider, "model": version},
+        "toolChoice": "auto",
+        "allowedAppDefaultToolkit": ["code", "visualization", "webSearch", "http", "connectors"],
+        "message": {
+            "role": "user",
+            "parts": [{"type": "text", "text": combined_text}],
+            "id": str(uuid.uuid4())
+        },
+        "imageTool": {},
+        "attachments": attachments
+    }
+
+def parse_cognix_stream_chunk(line):
+    if not line.strip(): return None, "content"
+    if line.startswith("data: "): line = line[6:]
+    if line.strip() == "[DONE]": return None, "stop"
+    
+    try:
+        data = json.loads(line)
+        # Handle various formats: 
+        # 1. {"text": "..."}
+        # 2. {"content": "..."}
+        # 3. {"delta": "..."} (Cognix format)
+        # 4. {"delta": {"text": "..."}} (OpenAI style)
+        # 5. {"type": "text-delta", "delta": "..."}
+        
+        content = data.get('text') or data.get('content')
+        if not content:
+            delta = data.get('delta')
+            if isinstance(delta, str):
+                content = delta
+            elif isinstance(delta, dict):
+                content = delta.get('text') or delta.get('content', '')
+        
+        return content or "", "content"
+    except:
+        # If it's not JSON, it might be raw text, but if it looks like JSON ({...}), 
+        # and parsing failed, we should probably ignore it to avoid garbage in content.
+        if line.strip().startswith('{') and line.strip().endswith('}'):
+            return "", "content"
+        return line, "content"
+
+# ============== Routes ==============
+
+@app.route('/v1/chat/completions', methods=['POST'])
+def chat_completions():
+    d = request.json
+    model = d.get('model', 'anthropic/Claude Opus 4.6')
+    messages = d.get('messages', [])
+    
+    # Extract system prompt
+    system_prompt = ""
+    filtered_messages = []
+    for m in messages:
+        if m.get('role') == 'system':
+            system_prompt = m.get('content', '')
+        else:
+            filtered_messages.append(m)
+            
+    prov, ver = model.split('/', 1) if '/' in model else ("anthropic", model)
+    payload = build_cognix_payload(filtered_messages, prov, ver, tools=d.get('tools'), system=system_prompt)
+    
+    if d.get('stream'):
+        def gen():
+            cid = f"chatcmpl-{uuid.uuid4().hex[:24]}"
+            yield f"data: {json.dumps({'id': cid, 'object': 'chat.completion.chunk', 'choices': [{'delta': {'role': 'assistant'}}]})}\n\n"
+            full_buf = ""
+            with requests.post(f"{COGNIX_BASE_URL}/api/chat", json=payload, headers=get_headers(), stream=True) as r:
+                for line in r.iter_lines(decode_unicode=True):
+                    if not line: continue
+                    cont, pty = parse_cognix_stream_chunk(line)
+                    if pty == "stop": break
+                    if cont:
+                        if d.get('tools'): full_buf += cont
+                        else: yield f"data: {json.dumps({'id': cid, 'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': cont}}]})}\n\n"
+            if d.get('tools') and full_buf:
+                txt, tcs = parse_tool_calls_from_response(full_buf)
+                if txt: yield f"data: {json.dumps({'id': cid, 'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': txt}}]})}\n\n"
+                if tcs:
+                    yield f"data: {json.dumps({'id': cid, 'object': 'chat.completion.chunk', 'choices': [{'delta': {'tool_calls': [{'index': 0, 'id': str(uuid.uuid4()), 'type': 'function', 'function': {'name': t['name'], 'arguments': json.dumps(t['input'])}}]}}]})}\n\n"
+            yield "data: [DONE]\n\n"
+        return Response(gen(), content_type='text/event-stream')
+    
+    r = requests.post(f"{COGNIX_BASE_URL}/api/chat", json=payload, headers=get_headers())
+    full_text = "".join([parse_cognix_stream_chunk(l)[0] or "" for l in r.text.strip().split('\n')])
+    txt, tcs = parse_tool_calls_from_response(full_text)
+    msg = {"role": "assistant", "content": txt or None}
+    if tcs: msg["tool_calls"] = [{"id": str(uuid.uuid4()), "type": "function", "function": {"name": t['name'], "arguments": json.dumps(t['input'])}} for t in tcs]
+    return jsonify({"id": str(uuid.uuid4()), "object": "chat.completion", "choices": [{"message": msg, "finish_reason": "tool_calls" if tcs else "stop"}]})
+
+@app.route('/v1/messages', methods=['POST'])
+def anthropic_messages():
+    d = request.json
+    model = d.get('model', 'claude-3-opus')
+    prov, ver = model.split('/', 1) if '/' in model else ("anthropic", model)
+    payload = build_cognix_payload(d.get('messages', []), prov, ver, tools=d.get('tools'), system=d.get('system'), tool_fmt="anthropic")
+    
+    if d.get('stream'):
+        def gen():
+            mid = f"msg_{uuid.uuid4().hex[:24]}"
+            yield f"event: message_start\ndata: {json.dumps({'type': 'message_start', 'message': {'id': mid, 'role': 'assistant', 'content': [], 'model': model}})}\n\n"
+            full_buf = ""
+            with requests.post(f"{COGNIX_BASE_URL}/api/chat", json=payload, headers=get_headers(), stream=True) as r:
+                for line in r.iter_lines(decode_unicode=True):
+                    if not line: continue
+                    cont, pty = parse_cognix_stream_chunk(line)
+                    if pty == "stop": break
+                    if cont:
+                        full_buf += cont
+                        if not d.get('tools'): yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': cont}})}\n\n"
+            if d.get('tools') and full_buf:
+                txt, tcs = parse_tool_calls_from_response(full_buf)
+                if txt: yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': txt}})}\n\n"
+                for tc in tcs:
+                    yield f"event: content_block_start\ndata: {json.dumps({'type': 'content_block_start', 'index': 1, 'content_block': {'type': 'tool_use', 'id': str(uuid.uuid4()), 'name': tc['name'], 'input': tc['input']}})}\n\n"
+            yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n"
+        return Response(gen(), content_type='text/event-stream')
+    
+    r = requests.post(f"{COGNIX_BASE_URL}/api/chat", json=payload, headers=get_headers())
+    full_text = "".join([parse_cognix_stream_chunk(l)[0] or "" for l in r.text.strip().split('\n')])
+    txt, tcs = parse_tool_calls_from_response(full_text)
+    content = [{"type": "text", "text": txt}] if txt else []
+    for t in tcs: content.append({"type": "tool_use", "id": str(uuid.uuid4()), "name": t['name'], "input": t['input']})
+    return jsonify({"id": str(uuid.uuid4()), "type": "message", "role": "assistant", "content": content, "model": model, "stop_reason": "tool_use" if tcs else "end_turn"})
+
+@app.route('/v1/files', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files: return jsonify({"error": "no file"}), 400
+    f = request.files['file']
+    fb = f.read()
+    mt = f.content_type or mimetypes.guess_type(f.filename)[0] or 'application/octet-stream'
+    fid = f"file-{uuid.uuid4().hex[:24]}"
+    files_cache[fid] = {"_data": base64.b64encode(fb).decode('utf-8'), "content_type": mt, "filename": f.filename}
+    return jsonify({"id": fid, "object": "file", "filename": f.filename, "purpose": "vision"})
+
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860, debug=True)